# Import libraries
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from scipy import stats
from scipy.stats import ttest_ind
# Load the data
all_district = pd.read_csv("data_csvs/all_by_district.csv")
girls_district = pd.read_csv("data_csvs/girls_by_district.csv")
all_state = pd.read_csv("data_csvs/all_by_state.csv")
girls_state = pd.read_csv("data_csvs/girls_by_state.csv")
district_dict = pd.read_csv("data_csvs/district_dict.csv")
state_dict = pd.read_csv("data_csvs/state_dict.csv")
all_district.head()
| Season_Start | District | All_Ages | 19&over | 17-18 | 15-16 | 13-14 | 12-Nov | 10-Sep | 8-Jul | 6&U | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2011 | Atlantic | 36178 | 11581 | 3264 | 4144 | 4229 | 4616 | 3903 | 2925 | 1516 |
| 1 | 2011 | Central | 58750 | 13970 | 3443 | 4870 | 6372 | 7354 | 8066 | 7309 | 7366 |
| 2 | 2011 | Massachusetts | 46788 | 4359 | 2529 | 4317 | 6451 | 7297 | 7527 | 7184 | 7124 |
| 3 | 2011 | Michigan | 52944 | 22852 | 2841 | 3900 | 4880 | 5117 | 4910 | 4353 | 4091 |
| 4 | 2011 | Mid-American | 35412 | 9815 | 2580 | 3281 | 3854 | 4282 | 4132 | 4008 | 3460 |
all_district.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 156 entries, 0 to 155 Data columns (total 11 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Season_Start 156 non-null int64 1 District 156 non-null object 2 All_Ages 156 non-null int64 3 19&over 156 non-null int64 4 17-18 156 non-null int64 5 15-16 156 non-null int64 6 13-14 156 non-null int64 7 12-Nov 156 non-null int64 8 10-Sep 156 non-null int64 9 8-Jul 156 non-null int64 10 6&U 156 non-null int64 dtypes: int64(10), object(1) memory usage: 13.5+ KB
girls_district.head()
| Season_Start | District | All_Ages | 19&over | 17-18 | 15-16 | 13-14 | 12-Nov | 10-Sep | 8-Jul | 6&U | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2011 | Atlantic | 2785 | 880 | 219 | 305 | 338 | 375 | 321 | 220 | 127 |
| 1 | 2011 | Central | 6860 | 1419 | 380 | 562 | 771 | 925 | 957 | 868 | 978 |
| 2 | 2011 | Massachusetts | 9564 | 1299 | 485 | 792 | 1249 | 1399 | 1471 | 1334 | 1535 |
| 3 | 2011 | Michigan | 4620 | 1741 | 251 | 377 | 419 | 512 | 470 | 417 | 433 |
| 4 | 2011 | Mid-American | 2873 | 770 | 184 | 238 | 277 | 335 | 346 | 342 | 381 |
girls_district.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 156 entries, 0 to 155 Data columns (total 11 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Season_Start 156 non-null int64 1 District 156 non-null object 2 All_Ages 156 non-null int64 3 19&over 156 non-null int64 4 17-18 156 non-null int64 5 15-16 156 non-null int64 6 13-14 156 non-null int64 7 12-Nov 156 non-null int64 8 10-Sep 156 non-null int64 9 8-Jul 156 non-null int64 10 6&U 156 non-null int64 dtypes: int64(10), object(1) memory usage: 13.5+ KB
all_state.head()
| Season_Start | District | State | All_Ages | 19&over | 17-18 | 15-16 | 13-14 | 12-Nov | 10-Sep | 8-Jul | 6&U | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2011 | Atlantic | DE | 1074 | 369 | 135 | 150 | 110 | 115 | 100 | 72 | 23 |
| 1 | 2011 | Atlantic | E PA | 17285 | 6664 | 1622 | 1924 | 1875 | 1905 | 1515 | 1165 | 615 |
| 2 | 2011 | Atlantic | NJ | 17819 | 4548 | 1507 | 2070 | 2244 | 2596 | 2288 | 1688 | 878 |
| 3 | 2011 | Central | IL | 26470 | 6713 | 1745 | 2545 | 2846 | 3301 | 3818 | 3096 | 2406 |
| 4 | 2011 | Central | IA | 3457 | 1009 | 176 | 261 | 300 | 374 | 394 | 438 | 505 |
all_state.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 636 entries, 0 to 635 Data columns (total 12 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Season_Start 636 non-null int64 1 District 636 non-null object 2 State 636 non-null object 3 All_Ages 636 non-null int64 4 19&over 636 non-null int64 5 17-18 636 non-null int64 6 15-16 636 non-null int64 7 13-14 636 non-null int64 8 12-Nov 636 non-null int64 9 10-Sep 636 non-null int64 10 8-Jul 636 non-null int64 11 6&U 636 non-null int64 dtypes: int64(10), object(2) memory usage: 59.8+ KB
girls_state.head()
| Season_Start | District | State | All_Ages | 19&over | 17-18 | 15-16 | 13-14 | 12-Nov | 10-Sep | 8-Jul | 6&U | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2011 | Atlantic | DE | 87 | 61 | 9 | 6 | 1 | 4 | 3 | 2 | 1 |
| 1 | 2011 | Atlantic | E PA | 1504 | 573 | 134 | 184 | 187 | 178 | 128 | 77 | 43 |
| 2 | 2011 | Atlantic | NJ | 1194 | 246 | 76 | 115 | 150 | 193 | 190 | 141 | 83 |
| 3 | 2011 | Central | IL | 2543 | 592 | 168 | 252 | 288 | 335 | 365 | 303 | 240 |
| 4 | 2011 | Central | IA | 249 | 51 | 22 | 25 | 24 | 27 | 32 | 24 | 44 |
girls_state.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 636 entries, 0 to 635 Data columns (total 12 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Season_Start 636 non-null int64 1 District 636 non-null object 2 State 636 non-null object 3 All_Ages 636 non-null int64 4 19&over 636 non-null int64 5 17-18 636 non-null int64 6 15-16 636 non-null int64 7 13-14 636 non-null int64 8 12-Nov 636 non-null int64 9 10-Sep 636 non-null int64 10 8-Jul 636 non-null int64 11 6&U 636 non-null int64 dtypes: int64(10), object(2) memory usage: 59.8+ KB
district_dict.head(13)
| District | District_Code | |
|---|---|---|
| 0 | Atlantic | 1 |
| 1 | Central | 2 |
| 2 | Massachusetts | 3 |
| 3 | Michigan | 4 |
| 4 | Mid-American | 5 |
| 5 | Minnesota | 6 |
| 6 | New England | 7 |
| 7 | New York | 8 |
| 8 | Northern Plains | 9 |
| 9 | Pacific | 10 |
| 10 | Rocky Mountain | 11 |
| 11 | Southeastern | 12 |
| 12 | All | 13 |
district_dict.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 13 entries, 0 to 12 Data columns (total 2 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 District 13 non-null object 1 District_Code 13 non-null int64 dtypes: int64(1), object(1) memory usage: 340.0+ bytes
state_dict.head()
| State | State_Code | |
|---|---|---|
| 0 | DE | 1.1 |
| 1 | E PA | 1.2 |
| 2 | NJ | 1.3 |
| 3 | IL | 2.1 |
| 4 | IA | 2.2 |
state_dict.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 53 entries, 0 to 52 Data columns (total 2 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 State 53 non-null object 1 State_Code 53 non-null float64 dtypes: float64(1), object(1) memory usage: 980.0+ bytes
Rename columns where it has been incorrectly transposed
def rename_columns(df):
"""
Rename specified columns in a DataFrame.
Args:
df: Pandas DataFrame
rename_dict: Dictionary containing old column names as keys and new column names as values
Returns:
DataFrame with specified columns renamed
"""
columns_to_rename = {'12-Nov': '11-12', '10-Sep': '9-10', '8-Jul': '7-8'}
df.rename(columns=columns_to_rename, inplace=True)
return df
# Example usage:
# Assuming df is your DataFrame
#df = rename_columns(df)
all_district_ = rename_columns(all_district)
girls_district = rename_columns(girls_district)
all_state = rename_columns(all_state)
girls_state = rename_columns(girls_state)
all_district.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 156 entries, 0 to 155 Data columns (total 11 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Season_Start 156 non-null int64 1 District 156 non-null object 2 All_Ages 156 non-null int64 3 19&over 156 non-null int64 4 17-18 156 non-null int64 5 15-16 156 non-null int64 6 13-14 156 non-null int64 7 11-12 156 non-null int64 8 9-10 156 non-null int64 9 7-8 156 non-null int64 10 6&U 156 non-null int64 dtypes: int64(10), object(1) memory usage: 13.5+ KB
def convert_columns_to_float(df):
"""
Convert specified columns in a DataFrame to float64 type.
Args:
df: Pandas DataFrame
columns: List of column names to convert to float64 type
Returns:
DataFrame with specified columns converted to float64 type
"""
# Define numeric columns
numeric_columns = ['Season_Start','All_Ages', '19&over', '17-18', '15-16', '13-14', '11-12', '9-10', '7-8', '6&U']
# Convert the selected columns to numeric
df[numeric_columns] = df[numeric_columns].apply(pd.to_numeric, errors='coerce').astype('float64')
return df
# Example usage:
# Assuming df is your DataFrame and numeric_columns is the list of columns to convert
#df = convert_columns_to_float(df)
all_district = convert_columns_to_float(all_district)
girls_district = convert_columns_to_float(girls_district)
all_state = convert_columns_to_float(all_state)
girls_state = convert_columns_to_float(girls_state)
all_state.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 636 entries, 0 to 635 Data columns (total 12 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Season_Start 636 non-null float64 1 District 636 non-null object 2 State 636 non-null object 3 All_Ages 636 non-null float64 4 19&over 636 non-null float64 5 17-18 636 non-null float64 6 15-16 636 non-null float64 7 13-14 636 non-null float64 8 11-12 636 non-null float64 9 9-10 636 non-null float64 10 7-8 636 non-null float64 11 6&U 636 non-null float64 dtypes: float64(10), object(2) memory usage: 59.8+ KB
girls_state.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 636 entries, 0 to 635 Data columns (total 12 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Season_Start 636 non-null float64 1 District 636 non-null object 2 State 636 non-null object 3 All_Ages 636 non-null float64 4 19&over 636 non-null float64 5 17-18 636 non-null float64 6 15-16 636 non-null float64 7 13-14 636 non-null float64 8 11-12 636 non-null float64 9 9-10 636 non-null float64 10 7-8 636 non-null float64 11 6&U 636 non-null float64 dtypes: float64(10), object(2) memory usage: 59.8+ KB
# Replace values in 'District' column
all_district['District'] = all_district['District'].map(district_dict.set_index('District')['District_Code'])
girls_district['District'] = girls_district['District'].map(district_dict.set_index('District')['District_Code'])
all_state['District'] = all_state['District'].map(district_dict.set_index('District')['District_Code'])
girls_state['District'] = girls_state['District'].map(district_dict.set_index('District')['District_Code'])
# Replace values in 'State' column
all_state['State'] = all_state['State'].map(state_dict.set_index('State')['State_Code'])
girls_state['State'] = girls_state['State'].map(state_dict.set_index('State')['State_Code'])
all_district.head(13)
| Season_Start | District | All_Ages | 19&over | 17-18 | 15-16 | 13-14 | 11-12 | 9-10 | 7-8 | 6&U | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2011.0 | 1 | 36178.0 | 11581.0 | 3264.0 | 4144.0 | 4229.0 | 4616.0 | 3903.0 | 2925.0 | 1516.0 |
| 1 | 2011.0 | 2 | 58750.0 | 13970.0 | 3443.0 | 4870.0 | 6372.0 | 7354.0 | 8066.0 | 7309.0 | 7366.0 |
| 2 | 2011.0 | 3 | 46788.0 | 4359.0 | 2529.0 | 4317.0 | 6451.0 | 7297.0 | 7527.0 | 7184.0 | 7124.0 |
| 3 | 2011.0 | 4 | 52944.0 | 22852.0 | 2841.0 | 3900.0 | 4880.0 | 5117.0 | 4910.0 | 4353.0 | 4091.0 |
| 4 | 2011.0 | 5 | 35412.0 | 9815.0 | 2580.0 | 3281.0 | 3854.0 | 4282.0 | 4132.0 | 4008.0 | 3460.0 |
| 5 | 2011.0 | 6 | 54951.0 | 8596.0 | 1963.0 | 3382.0 | 7227.0 | 8419.0 | 8527.0 | 8380.0 | 8457.0 |
| 6 | 2011.0 | 7 | 35343.0 | 5449.0 | 1827.0 | 3122.0 | 4481.0 | 5242.0 | 4897.0 | 4909.0 | 5416.0 |
| 7 | 2011.0 | 8 | 48205.0 | 13134.0 | 2710.0 | 4109.0 | 5382.0 | 6027.0 | 5887.0 | 5437.0 | 5519.0 |
| 8 | 2011.0 | 9 | 13701.0 | 3378.0 | 758.0 | 993.0 | 1428.0 | 1610.0 | 1811.0 | 1768.0 | 1955.0 |
| 9 | 2011.0 | 10 | 42540.0 | 24134.0 | 1958.0 | 2392.0 | 2890.0 | 3112.0 | 3016.0 | 2682.0 | 2356.0 |
| 10 | 2011.0 | 11 | 39815.0 | 17966.0 | 2711.0 | 3355.0 | 3672.0 | 3638.0 | 3426.0 | 3058.0 | 1989.0 |
| 11 | 2011.0 | 12 | 46551.0 | 20522.0 | 2807.0 | 3984.0 | 4101.0 | 4636.0 | 4279.0 | 3460.0 | 2762.0 |
| 12 | 2011.0 | 13 | 511178.0 | 155756.0 | 29391.0 | 41849.0 | 54967.0 | 61350.0 | 60381.0 | 55473.0 | 52011.0 |
all_state.tail()
| Season_Start | District | State | All_Ages | 19&over | 17-18 | 15-16 | 13-14 | 11-12 | 9-10 | 7-8 | 6&U | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 631 | 2022.0 | 12 | 12.90 | 7654.0 | 2836.0 | 350.0 | 569.0 | 737.0 | 902.0 | 969.0 | 780.0 | 511.0 |
| 632 | 2022.0 | 12 | 12.91 | 3008.0 | 1852.0 | 97.0 | 133.0 | 194.0 | 186.0 | 201.0 | 200.0 | 145.0 |
| 633 | 2022.0 | 12 | 12.92 | 4833.0 | 2081.0 | 275.0 | 375.0 | 382.0 | 404.0 | 440.0 | 490.0 | 386.0 |
| 634 | 2022.0 | 12 | 12.93 | 10548.0 | 4427.0 | 601.0 | 876.0 | 972.0 | 1051.0 | 1048.0 | 989.0 | 584.0 |
| 635 | 2022.0 | 13 | 13.00 | 556186.0 | 168276.0 | 30895.0 | 46836.0 | 59141.0 | 63490.0 | 65415.0 | 63313.0 | 58820.0 |
# Selecting relevant columns from all_district and girls_district
all_district_subset = all_district[['Season_Start', 'District', 'All_Ages', '19&over', '17-18', '15-16', '13-14', '11-12', '9-10', '7-8', '6&U']]
girls_district_subset = girls_district[['Season_Start', 'District', 'All_Ages', '19&over', '17-18', '15-16', '13-14', '11-12', '9-10', '7-8', '6&U']]
# Subtracting girls_district values from all_district values
boys_district = all_district_subset.copy()
boys_district[['All_Ages', '19&over', '17-18', '15-16', '13-14', '11-12', '9-10', '7-8', '6&U']] -= girls_district_subset[['All_Ages', '19&over', '17-18', '15-16', '13-14', '11-12', '9-10', '7-8', '6&U']]
# Displaying the resulting DataFrame (boys_df)
boys_district.head()
| Season_Start | District | All_Ages | 19&over | 17-18 | 15-16 | 13-14 | 11-12 | 9-10 | 7-8 | 6&U | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2011.0 | 1 | 33393.0 | 10701.0 | 3045.0 | 3839.0 | 3891.0 | 4241.0 | 3582.0 | 2705.0 | 1389.0 |
| 1 | 2011.0 | 2 | 51890.0 | 12551.0 | 3063.0 | 4308.0 | 5601.0 | 6429.0 | 7109.0 | 6441.0 | 6388.0 |
| 2 | 2011.0 | 3 | 37224.0 | 3060.0 | 2044.0 | 3525.0 | 5202.0 | 5898.0 | 6056.0 | 5850.0 | 5589.0 |
| 3 | 2011.0 | 4 | 48324.0 | 21111.0 | 2590.0 | 3523.0 | 4461.0 | 4605.0 | 4440.0 | 3936.0 | 3658.0 |
| 4 | 2011.0 | 5 | 32539.0 | 9045.0 | 2396.0 | 3043.0 | 3577.0 | 3947.0 | 3786.0 | 3666.0 | 3079.0 |
# Selecting relevant columns from all_district and girls_district
all_state_subset = all_state[['Season_Start', 'District', 'State', 'All_Ages', '19&over', '17-18', '15-16', '13-14', '11-12', '9-10', '7-8', '6&U']]
girls_state_subset = girls_state[['Season_Start', 'District', 'State', 'All_Ages', '19&over', '17-18', '15-16', '13-14', '11-12', '9-10', '7-8', '6&U']]
# Subtracting girls_district values from all_district values
boys_state = all_state_subset.copy()
boys_state[['All_Ages', '19&over', '17-18', '15-16', '13-14', '11-12', '9-10', '7-8', '6&U']] -= girls_state_subset[['All_Ages', '19&over', '17-18', '15-16', '13-14', '11-12', '9-10', '7-8', '6&U']]
# Displaying the resulting DataFrame (boys_df)
boys_state.head()
| Season_Start | District | State | All_Ages | 19&over | 17-18 | 15-16 | 13-14 | 11-12 | 9-10 | 7-8 | 6&U | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2011.0 | 1 | 1.1 | 987.0 | 308.0 | 126.0 | 144.0 | 109.0 | 111.0 | 97.0 | 70.0 | 22.0 |
| 1 | 2011.0 | 1 | 1.2 | 15781.0 | 6091.0 | 1488.0 | 1740.0 | 1688.0 | 1727.0 | 1387.0 | 1088.0 | 572.0 |
| 2 | 2011.0 | 1 | 1.3 | 16625.0 | 4302.0 | 1431.0 | 1955.0 | 2094.0 | 2403.0 | 2098.0 | 1547.0 | 795.0 |
| 3 | 2011.0 | 2 | 2.1 | 23927.0 | 6121.0 | 1577.0 | 2293.0 | 2558.0 | 2966.0 | 3453.0 | 2793.0 | 2166.0 |
| 4 | 2011.0 | 2 | 2.2 | 3208.0 | 958.0 | 154.0 | 236.0 | 276.0 | 347.0 | 362.0 | 414.0 | 461.0 |
boys_district.describe()
| Season_Start | District | All_Ages | 19&over | 17-18 | 15-16 | 13-14 | 11-12 | 9-10 | 7-8 | 6&U | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 156.00000 | 156.000000 | 156.000000 | 156.000000 | 156.000000 | 156.000000 | 156.000000 | 156.000000 | 156.000000 | 156.000000 | 156.000000 |
| mean | 2016.50000 | 7.000000 | 70600.461538 | 22935.461538 | 3996.166667 | 5842.269231 | 7534.820513 | 8046.051282 | 8024.076923 | 7248.076923 | 6827.371795 |
| std | 3.46317 | 3.753708 | 113192.764057 | 37505.166464 | 6401.052648 | 9361.134520 | 12072.153655 | 12897.002383 | 12870.998331 | 11665.633093 | 11055.000438 |
| min | 2011.00000 | 1.000000 | 10981.000000 | 2419.000000 | 535.000000 | 732.000000 | 1075.000000 | 1274.000000 | 1436.000000 | 1463.000000 | 785.000000 |
| 25% | 2013.75000 | 4.000000 | 33591.750000 | 6708.750000 | 1777.750000 | 2727.750000 | 3553.000000 | 3632.250000 | 3526.750000 | 3018.750000 | 2346.250000 |
| 50% | 2016.50000 | 7.000000 | 41267.000000 | 11945.500000 | 2356.000000 | 3319.500000 | 3963.500000 | 4160.500000 | 4130.500000 | 3716.000000 | 3472.000000 |
| 75% | 2019.25000 | 10.000000 | 46338.000000 | 21158.500000 | 2632.250000 | 3891.500000 | 5499.250000 | 5960.500000 | 6072.000000 | 5285.250000 | 5414.500000 |
| max | 2022.00000 | 13.000000 | 485100.000000 | 162647.000000 | 28005.000000 | 41025.000000 | 50585.000000 | 54437.000000 | 54628.000000 | 50949.000000 | 49619.000000 |
boys_district.describe()
| Season_Start | District | All_Ages | 19&over | 17-18 | 15-16 | 13-14 | 11-12 | 9-10 | 7-8 | 6&U | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 156.00000 | 156.000000 | 156.000000 | 156.000000 | 156.000000 | 156.000000 | 156.000000 | 156.000000 | 156.000000 | 156.000000 | 156.000000 |
| mean | 2016.50000 | 7.000000 | 70600.461538 | 22935.461538 | 3996.166667 | 5842.269231 | 7534.820513 | 8046.051282 | 8024.076923 | 7248.076923 | 6827.371795 |
| std | 3.46317 | 3.753708 | 113192.764057 | 37505.166464 | 6401.052648 | 9361.134520 | 12072.153655 | 12897.002383 | 12870.998331 | 11665.633093 | 11055.000438 |
| min | 2011.00000 | 1.000000 | 10981.000000 | 2419.000000 | 535.000000 | 732.000000 | 1075.000000 | 1274.000000 | 1436.000000 | 1463.000000 | 785.000000 |
| 25% | 2013.75000 | 4.000000 | 33591.750000 | 6708.750000 | 1777.750000 | 2727.750000 | 3553.000000 | 3632.250000 | 3526.750000 | 3018.750000 | 2346.250000 |
| 50% | 2016.50000 | 7.000000 | 41267.000000 | 11945.500000 | 2356.000000 | 3319.500000 | 3963.500000 | 4160.500000 | 4130.500000 | 3716.000000 | 3472.000000 |
| 75% | 2019.25000 | 10.000000 | 46338.000000 | 21158.500000 | 2632.250000 | 3891.500000 | 5499.250000 | 5960.500000 | 6072.000000 | 5285.250000 | 5414.500000 |
| max | 2022.00000 | 13.000000 | 485100.000000 | 162647.000000 | 28005.000000 | 41025.000000 | 50585.000000 | 54437.000000 | 54628.000000 | 50949.000000 | 49619.000000 |
boys_state.describe()
| Season_Start | District | State | All_Ages | 19&over | 17-18 | 15-16 | 13-14 | 11-12 | 9-10 | 7-8 | 6&U | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 636.00000 | 636.000000 | 636.000000 | 636.000000 | 636.000000 | 636.000000 | 636.000000 | 636.000000 | 636.000000 | 636.000000 | 636.000000 | 636.000000 |
| mean | 2016.50000 | 8.037736 | 8.393585 | 17317.094340 | 5624.116352 | 970.616352 | 1426.559748 | 1847.827044 | 1980.191824 | 1979.572327 | 1794.974843 | 1693.235849 |
| std | 3.45477 | 3.804493 | 3.906673 | 62364.684175 | 20380.952585 | 3490.383500 | 5139.783536 | 6670.693158 | 7148.075764 | 7147.883248 | 6497.035939 | 6164.764226 |
| min | 2011.00000 | 1.000000 | 1.100000 | 12.000000 | 11.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 2013.75000 | 5.000000 | 5.300000 | 1695.000000 | 490.500000 | 84.000000 | 112.000000 | 134.750000 | 156.250000 | 160.000000 | 148.750000 | 133.750000 |
| 50% | 2016.50000 | 9.000000 | 9.400000 | 4552.000000 | 1514.000000 | 249.500000 | 351.000000 | 473.500000 | 530.000000 | 529.500000 | 468.000000 | 435.500000 |
| 75% | 2019.25000 | 11.000000 | 11.700000 | 12167.000000 | 3813.500000 | 645.500000 | 958.000000 | 1351.500000 | 1375.000000 | 1338.250000 | 1121.500000 | 898.750000 |
| max | 2022.00000 | 13.000000 | 13.000000 | 485100.000000 | 162647.000000 | 26733.000000 | 39738.000000 | 50585.000000 | 54437.000000 | 54628.000000 | 50949.000000 | 49619.000000 |
girls_state.describe()
| Season_Start | District | State | All_Ages | 19&over | 17-18 | 15-16 | 13-14 | 11-12 | 9-10 | 7-8 | 6&U | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 636.00000 | 636.000000 | 636.000000 | 636.000000 | 636.000000 | 636.000000 | 636.000000 | 636.000000 | 636.000000 | 636.000000 | 636.000000 | 636.000000 |
| mean | 2016.50000 | 8.037736 | 8.393585 | 2874.723270 | 661.172956 | 134.544025 | 223.496855 | 322.798742 | 377.723270 | 392.591195 | 368.723270 | 393.672956 |
| std | 3.45477 | 3.804493 | 3.906673 | 10531.163617 | 2401.003729 | 491.544789 | 818.781356 | 1186.743796 | 1394.370635 | 1455.158308 | 1378.563964 | 1472.338726 |
| min | 2011.00000 | 1.000000 | 1.100000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 2013.75000 | 5.000000 | 5.300000 | 189.000000 | 52.000000 | 8.000000 | 12.000000 | 18.000000 | 21.000000 | 23.000000 | 20.000000 | 21.750000 |
| 50% | 2016.50000 | 9.000000 | 9.400000 | 710.500000 | 164.000000 | 33.000000 | 54.000000 | 66.500000 | 75.000000 | 77.000000 | 73.500000 | 73.000000 |
| 75% | 2019.25000 | 11.000000 | 11.700000 | 1470.500000 | 430.000000 | 75.000000 | 119.250000 | 165.250000 | 182.250000 | 185.000000 | 171.250000 | 186.000000 |
| max | 2022.00000 | 13.000000 | 13.000000 | 91254.000000 | 19528.000000 | 4340.000000 | 7098.000000 | 10146.000000 | 11927.000000 | 12816.000000 | 12793.000000 | 12972.000000 |
The district dataframes are:
all_district
boys_district
girls_district
Calculate the growth percentage season to season for the boys and the girls to compare the growth rates.
def calculate_season_to_season_growth(df):
"""
Calculates season-to-season growth for each district in the given DataFrame.
Parameters:
- df: DataFrame with columns 'Season_Start', 'District', and numeric columns for age groups.
Returns:
- growth_df: DataFrame with season-to-season growth for each district including the seasons being compared.
"""
# Sort DataFrame by 'Season_Start' for chronological order
df = df.sort_values(by='Season_Start')
# Group by 'District' and calculate growth
growth_df = pd.DataFrame()
for district, group_df in df.groupby('District'):
# Calculate growth for numeric columns
growth = group_df.set_index('Season_Start').pct_change().dropna() * 100
growth['District'] = district
growth.reset_index(inplace=True)
# Add columns for the seasons being compared
growth['Season_1_Compared'] = growth['Season_Start'].shift(1)
growth['Season_2_Compared'] = growth['Season_Start']
growth_df = pd.concat([growth_df, growth], ignore_index=True)
return growth_df
boys_district_growth = calculate_season_to_season_growth(boys_district)
girls_district_growth = calculate_season_to_season_growth(girls_district)
boys_district_growth.head()
| Season_Start | District | All_Ages | 19&over | 17-18 | 15-16 | 13-14 | 11-12 | 9-10 | 7-8 | 6&U | Season_1_Compared | Season_2_Compared | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2012.0 | 1 | 0.973258 | 7.419867 | -2.725780 | -2.188070 | 2.981239 | -3.442584 | -2.847571 | -3.475046 | -5.471562 | NaN | 2012.0 |
| 1 | 2013.0 | 1 | 1.097337 | -1.618095 | -4.456448 | -1.970706 | 2.994759 | 0.830281 | 5.775862 | 5.898123 | 19.268850 | 2012.0 | 2013.0 |
| 2 | 2014.0 | 1 | -0.868341 | 0.123795 | -1.201413 | 0.977995 | 1.526533 | -0.726568 | -3.205651 | -3.182640 | -8.876117 | 2013.0 | 2014.0 |
| 3 | 2015.0 | 1 | -0.396544 | 1.969443 | 0.572246 | -0.430455 | -0.835322 | 1.244206 | -4.771260 | -4.482630 | -5.816398 | 2014.0 | 2015.0 |
| 4 | 2016.0 | 1 | 2.929467 | 0.363762 | -0.568990 | 2.864091 | -0.890493 | -4.120482 | 1.532567 | 13.179507 | 50.074405 | 2015.0 | 2016.0 |
girls_district_growth.head()
| Season_Start | District | All_Ages | 19&over | 17-18 | 15-16 | 13-14 | 11-12 | 9-10 | 7-8 | 6&U | Season_1_Compared | Season_2_Compared | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2012.0 | 1 | -2.585278 | -2.386364 | -9.132420 | -3.934426 | 0.000000 | 7.466667 | -9.034268 | -2.272727 | -10.236220 | NaN | 2012.0 |
| 1 | 2013.0 | 1 | 8.072245 | 4.190920 | 5.025126 | -2.389078 | 20.710059 | -4.714640 | 6.849315 | 15.348837 | 66.666667 | 2012.0 | 2013.0 |
| 2 | 2014.0 | 1 | 4.229195 | 0.223464 | 2.392344 | 27.272727 | 11.274510 | -2.864583 | 13.141026 | 1.209677 | -21.052632 | 2013.0 | 2014.0 |
| 3 | 2015.0 | 1 | 4.679319 | -2.564103 | -8.411215 | 15.659341 | -10.792952 | 13.136729 | 19.546742 | 11.952191 | 18.666667 | 2014.0 | 2015.0 |
| 4 | 2016.0 | 1 | 5.345420 | -0.572082 | 13.265306 | -10.688836 | -2.469136 | 5.924171 | -4.502370 | 29.893238 | 64.606742 | 2015.0 | 2016.0 |
boys_district_growth['Season_1_Compared'].fillna(2011, inplace=True)
girls_district_growth['Season_1_Compared'].fillna(2011, inplace=True)
boys_district_growth.head(13)
| Season_Start | District | All_Ages | 19&over | 17-18 | 15-16 | 13-14 | 11-12 | 9-10 | 7-8 | 6&U | Season_1_Compared | Season_2_Compared | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2012.0 | 1 | 0.973258 | 7.419867 | -2.725780 | -2.188070 | 2.981239 | -3.442584 | -2.847571 | -3.475046 | -5.471562 | 2011.0 | 2012.0 |
| 1 | 2013.0 | 1 | 1.097337 | -1.618095 | -4.456448 | -1.970706 | 2.994759 | 0.830281 | 5.775862 | 5.898123 | 19.268850 | 2012.0 | 2013.0 |
| 2 | 2014.0 | 1 | -0.868341 | 0.123795 | -1.201413 | 0.977995 | 1.526533 | -0.726568 | -3.205651 | -3.182640 | -8.876117 | 2013.0 | 2014.0 |
| 3 | 2015.0 | 1 | -0.396544 | 1.969443 | 0.572246 | -0.430455 | -0.835322 | 1.244206 | -4.771260 | -4.482630 | -5.816398 | 2014.0 | 2015.0 |
| 4 | 2016.0 | 1 | 2.929467 | 0.363762 | -0.568990 | 2.864091 | -0.890493 | -4.120482 | 1.532567 | 13.179507 | 50.074405 | 2015.0 | 2016.0 |
| 5 | 2017.0 | 1 | -1.177693 | -6.178806 | -3.540773 | 0.157604 | -0.704225 | -2.287007 | 4.876633 | 9.122322 | 4.412494 | 2016.0 | 2017.0 |
| 6 | 2018.0 | 1 | 1.854773 | 6.374172 | 2.892102 | -6.136900 | -5.649303 | -1.671811 | 3.598118 | 1.013300 | 11.016144 | 2017.0 | 2018.0 |
| 7 | 2019.0 | 1 | -5.471595 | -5.603113 | -7.099099 | -2.151439 | -2.177294 | -3.766675 | -4.408229 | -8.369906 | -13.943541 | 2018.0 | 2019.0 |
| 8 | 2020.0 | 1 | -18.086946 | -31.244847 | 0.271528 | -1.085094 | -5.537891 | -5.028540 | -12.381219 | -21.484776 | -41.252485 | 2019.0 | 2020.0 |
| 9 | 2021.0 | 1 | 15.751268 | 40.527578 | -4.835590 | -4.012702 | -0.504909 | 0.400687 | 2.232855 | 24.270153 | 53.045685 | 2020.0 | 2021.0 |
| 10 | 2022.0 | 1 | 0.419146 | -0.815320 | -0.650407 | -0.721805 | 0.873978 | -2.936146 | 1.372855 | 6.171108 | 6.025428 | 2021.0 | 2022.0 |
| 11 | 2012.0 | 2 | 0.676431 | 7.099036 | 1.632387 | -2.948004 | 2.035351 | 2.270960 | -1.266001 | -1.940692 | -7.952411 | 2011.0 | 2012.0 |
| 12 | 2013.0 | 2 | 4.806570 | 5.095968 | -13.684549 | -16.981583 | -34.873141 | -45.460076 | -56.518023 | -64.898670 | -86.649660 | 2012.0 | 2013.0 |
girls_district_growth.head(13)
| Season_Start | District | All_Ages | 19&over | 17-18 | 15-16 | 13-14 | 11-12 | 9-10 | 7-8 | 6&U | Season_1_Compared | Season_2_Compared | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2012.0 | 1 | -2.585278 | -2.386364 | -9.132420 | -3.934426 | 0.000000 | 7.466667 | -9.034268 | -2.272727 | -10.236220 | 2011.0 | 2012.0 |
| 1 | 2013.0 | 1 | 8.072245 | 4.190920 | 5.025126 | -2.389078 | 20.710059 | -4.714640 | 6.849315 | 15.348837 | 66.666667 | 2012.0 | 2013.0 |
| 2 | 2014.0 | 1 | 4.229195 | 0.223464 | 2.392344 | 27.272727 | 11.274510 | -2.864583 | 13.141026 | 1.209677 | -21.052632 | 2013.0 | 2014.0 |
| 3 | 2015.0 | 1 | 4.679319 | -2.564103 | -8.411215 | 15.659341 | -10.792952 | 13.136729 | 19.546742 | 11.952191 | 18.666667 | 2014.0 | 2015.0 |
| 4 | 2016.0 | 1 | 5.345420 | -0.572082 | 13.265306 | -10.688836 | -2.469136 | 5.924171 | -4.502370 | 29.893238 | 64.606742 | 2015.0 | 2016.0 |
| 5 | 2017.0 | 1 | 7.329377 | 1.150748 | 15.765766 | -6.914894 | 10.886076 | 7.382550 | 9.181141 | 21.917808 | 11.945392 | 2016.0 | 2017.0 |
| 6 | 2018.0 | 1 | 3.400608 | 1.592719 | -5.836576 | 8.857143 | 3.881279 | -2.083333 | 16.363636 | 2.696629 | 0.609756 | 2017.0 | 2018.0 |
| 7 | 2019.0 | 1 | 4.010695 | 1.231803 | -2.066116 | 8.661417 | 13.186813 | 2.127660 | 5.859375 | 5.908096 | -4.848485 | 2018.0 | 2019.0 |
| 8 | 2020.0 | 1 | -20.000000 | -55.088496 | -87.341772 | -42.753623 | -20.776699 | -3.541667 | -13.837638 | -2.066116 | 28.025478 | 2019.0 | 2020.0 |
| 9 | 2021.0 | 1 | 31.266067 | 126.847291 | 863.333333 | 113.502110 | 20.098039 | 14.038877 | 17.130621 | -0.843882 | -16.915423 | 2020.0 | 2021.0 |
| 10 | 2022.0 | 1 | 4.724602 | 3.148751 | 20.415225 | -0.395257 | 4.285714 | 3.787879 | 5.301645 | 9.787234 | -2.694611 | 2021.0 | 2022.0 |
| 11 | 2012.0 | 2 | -2.055394 | 3.171247 | 0.789474 | -7.295374 | 0.129702 | -2.594595 | 2.507837 | -5.529954 | -10.327198 | 2011.0 | 2012.0 |
| 12 | 2013.0 | 2 | 2.217592 | 7.377049 | -8.093995 | -4.798464 | 5.310881 | 2.885683 | -4.077472 | -2.926829 | 10.718358 | 2012.0 | 2013.0 |
def calculate_district_growth(df, district_id):
# Filter data for the specified district
district_data = df[df['District'] == district_id].copy()
# Extract data for the 2011 and 2022 seasons
season_2011 = district_data[district_data['Season_Start'] == 2011]
season_2022 = district_data[district_data['Season_Start'] == 2022]
# Calculate growth for each age group
growth = (season_2022.iloc[:, 2:] - season_2011.iloc[:, 2:]) / season_2011.iloc[:, 2:] * 100
# Add columns for start and end seasons
growth['Season_1'] = season_2011['Season_Start'].values[0]
growth['Season_2'] = season_2022['Season_Start'].values[0]
# Add a row for overall growth
overall_growth = pd.DataFrame({
'District': [district_id],
'All_Ages': [growth['All_Ages'].mean()],
'19&over': [growth['19&over'].mean()],
'17-18': [growth['17-18'].mean()],
'15-16': [growth['15-16'].mean()],
'13-14': [growth['13-14'].mean()],
'11-12': [growth['11-12'].mean()],
'9-10': [growth['9-10'].mean()],
'7-8': [growth['7-8'].mean()],
'6&U': [growth['6&U'].mean()],
'Season_1': [season_2011['Season_Start'].values[0]],
'Season_2': [season_2022['Season_Start'].values[0]]
})
# Concatenate the individual age group growth and overall growth
result = pd.concat([growth, overall_growth], ignore_index=True)
return result
# Example usage:
# district_id = 1 # Replace with the desired district ID
# district_growth = calculate_district_growth(all_district, district_id)
# print(district_growth)
growth_boys_district_13 = boys_district[(boys_district['District'] == 13) & ((boys_district['Season_Start'] == 2022) | (boys_district['Season_Start'] == 2011))]
growth_boys_district_13 = growth_boys_district_13.set_index('Season_Start').diff().iloc[-1]
growth_boys_district_13 = growth_boys_district_13 / boys_district[all_district['Season_Start'] == 2011].set_index('Season_Start').iloc[0] * 100
print(growth_boys_district_13)
District 0.000000 All_Ages 61.228401 19&over 90.785908 17-18 12.939245 15-16 77.337848 13-14 39.321511 11-12 -17.495874 9-10 31.323283 7-8 111.386322 6&U 176.025918 dtype: float64
growth_girls_district_13 = girls_district[(boys_district['District'] == 13) & ((girls_district['Season_Start'] == 2022) | (girls_district['Season_Start'] == 2011))]
growth_girls_district_13 = growth_girls_district_13.set_index('Season_Start').diff().iloc[-1]
growth_girls_district_13 = growth_girls_district_13 / girls_district[all_district['Season_Start'] == 2011].set_index('Season_Start').iloc[0] * 100
print(growth_girls_district_13)
District 0.000000 All_Ages 881.938959 19&over 318.750000 17-18 506.849315 15-16 661.639344 13-14 782.248521 11-12 768.533333 9-10 1218.691589 7-8 2194.090909 6&U 3436.220472 dtype: float64
# Assuming boys_district_growth and girls_district_growth DataFrames
age_groups = ['All_Ages', '19&over', '17-18', '15-16', '13-14', '11-12', '9-10', '7-8', '6&U']
for age_group in age_groups:
boys_growth = boys_district_growth[age_group].dropna()
girls_growth = girls_district_growth[age_group].dropna()
# Summary Statistics
boys_summary = boys_growth.describe()
girls_summary = girls_growth.describe()
print(f"Summary Statistics for {age_group} - Boys:")
print(boys_summary)
print(f"\nSummary Statistics for {age_group} - Girls:")
print(girls_summary)
# T-test
t_statistic, p_value = stats.ttest_ind(boys_growth, girls_growth, equal_var=False)
print(f"\nT-test results for {age_group}:")
print(f"T-statistic: {t_statistic}")
print(f"P-value: {p_value}")
# Check if the difference is significant at a 95% confidence level
alpha = 0.05
if p_value < alpha:
print("\nThe difference is statistically significant.\n")
else:
print("\nThe difference is not statistically significant.\n")
Summary Statistics for All_Ages - Boys: count 143.000000 mean 0.951232 std 10.696090 min -47.331176 25% -1.032265 50% 0.864688 75% 2.906340 max 78.580153 Name: All_Ages, dtype: float64 Summary Statistics for All_Ages - Girls: count 143.000000 mean 3.831219 std 11.687997 min -42.140241 25% 0.671382 50% 3.400608 75% 6.613878 max 75.621191 Name: All_Ages, dtype: float64 T-test results for All_Ages: T-statistic: -2.173742633617583 P-value: 0.030556936933928032 The difference is statistically significant. Summary Statistics for 19&over - Boys: count 143.000000 mean 2.222269 std 20.469001 min -61.725510 25% -1.722565 50% 1.628077 75% 5.717506 max 146.894973 Name: 19&over, dtype: float64 Summary Statistics for 19&over - Girls: count 143.000000 mean 5.252990 std 32.134173 min -62.699387 25% -1.342725 50% 1.596916 75% 5.746109 max 155.458515 Name: 19&over, dtype: float64 T-test results for 19&over: T-statistic: -0.9512455803908627 P-value: 0.3424329532873308 The difference is not statistically significant. Summary Statistics for 17-18 - Boys: count 143.000000 mean 0.593595 std 8.395509 min -29.849057 25% -2.646057 50% -0.207727 75% 2.635111 max 52.298851 Name: 17-18, dtype: float64 Summary Statistics for 17-18 - Girls: count 143.000000 mean 70.349035 std 282.418056 min -94.942529 25% -4.942631 50% 2.265372 75% 8.493645 max 1904.545455 Name: 17-18, dtype: float64 T-test results for 17-18: T-statistic: -2.952309135185616 P-value: 0.0036908172111356522 The difference is statistically significant. Summary Statistics for 15-16 - Boys: count 143.000000 mean 1.045745 std 6.243847 min -21.761281 25% -1.796867 50% 0.977995 75% 3.379656 max 36.354615 Name: 15-16, dtype: float64 Summary Statistics for 15-16 - Girls: count 143.000000 mean 6.390607 std 27.327220 min -50.115473 25% -2.822217 50% 3.929916 75% 9.047655 max 125.462963 Name: 15-16, dtype: float64 T-test results for 15-16: T-statistic: -2.280125930979017 P-value: 0.023947992795947972 The difference is statistically significant. Summary Statistics for 13-14 - Boys: count 143.000000 mean 0.649856 std 7.174630 min -34.873141 25% -1.498996 50% 0.571874 75% 2.476904 max 63.218700 Name: 13-14, dtype: float64 Summary Statistics for 13-14 - Girls: count 143.000000 mean 4.880664 std 18.163781 min -34.168157 25% -2.133861 50% 2.118644 75% 7.397198 max 85.869565 Name: 13-14, dtype: float64 T-test results for 13-14: T-statistic: -2.5906097359693576 P-value: 0.010343043416480518 The difference is statistically significant. Summary Statistics for 11-12 - Boys: count 143.000000 mean 0.499813 std 10.532733 min -45.460076 25% -2.329878 50% -0.198659 75% 2.765759 max 101.505856 Name: 11-12, dtype: float64 Summary Statistics for 11-12 - Girls: count 143.000000 mean 4.102480 std 11.635099 min -30.040595 25% -1.396142 50% 2.486679 75% 8.443674 max 46.851385 Name: 11-12, dtype: float64 T-test results for 11-12: T-statistic: -2.745033120880804 P-value: 0.006440082127097802 The difference is statistically significant. Summary Statistics for 9-10 - Boys: count 143.000000 mean 1.256297 std 14.968338 min -56.518023 25% -2.185239 50% 0.573066 75% 3.339835 max 145.937090 Name: 9-10, dtype: float64 Summary Statistics for 9-10 - Girls: count 143.000000 mean 4.951924 std 12.206388 min -30.386052 25% -0.940258 50% 2.824268 75% 8.492017 max 56.171735 Name: 9-10, dtype: float64 T-test results for 9-10: T-statistic: -2.288096703813683 P-value: 0.022896576713507373 The difference is statistically significant. Summary Statistics for 7-8 - Boys: count 143.000000 mean 2.417437 std 20.156915 min -64.898670 25% -3.682709 50% 1.343101 75% 5.853801 max 180.108254 Name: 7-8, dtype: float64 Summary Statistics for 7-8 - Girls: count 143.000000 mean 6.164154 std 11.383932 min -31.350682 25% -0.081384 50% 5.409467 75% 11.483813 max 56.403270 Name: 7-8, dtype: float64 T-test results for 7-8: T-statistic: -1.9354371588970642 P-value: 0.05419420412246262 The difference is not statistically significant. Summary Statistics for 6&U - Boys: count 143.000000 mean 6.777624 std 62.291285 min -86.649660 25% -3.023863 50% 0.576067 75% 5.984290 max 711.719745 Name: 6&U, dtype: float64 Summary Statistics for 6&U - Girls: count 143.000000 mean 6.691576 std 19.412449 min -42.794118 25% -3.241717 50% 4.582651 75% 13.305609 max 70.951157 Name: 6&U, dtype: float64 T-test results for 6&U: T-statistic: 0.015770823907187034 P-value: 0.9874357918863546 The difference is not statistically significant.
# Combine the boys and girls growth DataFrames
combined_growth = pd.merge(boys_district_growth, girls_district_growth, on=['Season_Start', 'District'], suffixes=('_boys', '_girls'))
# Display the combined growth DataFrame
print(combined_growth)
Season_Start District All_Ages_boys 19&over_boys 17-18_boys \
0 2012.0 1 0.973258 7.419867 -2.725780
1 2013.0 1 1.097337 -1.618095 -4.456448
2 2014.0 1 -0.868341 0.123795 -1.201413
3 2015.0 1 -0.396544 1.969443 0.572246
4 2016.0 1 2.929467 0.363762 -0.568990
.. ... ... ... ... ...
138 2018.0 13 0.478469 0.860727 1.320340
139 2019.0 13 -1.546485 -2.231212 -0.868756
140 2020.0 13 -19.717419 -36.692073 9.565728
141 2021.0 13 19.829016 44.585829 -4.542046
142 2022.0 13 1.191404 2.192970 -0.665844
15-16_boys 13-14_boys 11-12_boys 9-10_boys 7-8_boys ... \
0 -2.188070 2.981239 -3.442584 -2.847571 -3.475046 ...
1 -1.970706 2.994759 0.830281 5.775862 5.898123 ...
2 0.977995 1.526533 -0.726568 -3.205651 -3.182640 ...
3 -0.430455 -0.835322 1.244206 -4.771260 -4.482630 ...
4 2.864091 -0.890493 -4.120482 1.532567 13.179507 ...
.. ... ... ... ... ... ...
138 0.702840 -1.496491 0.254185 1.358171 0.035342 ...
139 0.747415 -1.027535 -0.815085 -1.131288 -1.897976 ...
140 6.046115 1.161895 -4.605014 -10.803555 -20.825497 ...
141 -4.994516 -1.858125 2.042477 9.722885 22.073131 ...
142 1.955049 0.067399 0.299558 -0.491875 4.578952 ...
19&over_girls 17-18_girls 15-16_girls 13-14_girls 11-12_girls \
0 -2.386364 -9.132420 -3.934426 0.000000 7.466667
1 4.190920 5.025126 -2.389078 20.710059 -4.714640
2 0.223464 2.392344 27.272727 11.274510 -2.864583
3 -2.564103 -8.411215 15.659341 -10.792952 13.136729
4 -0.572082 13.265306 -10.688836 -2.469136 5.924171
.. ... ... ... ... ...
138 4.060527 2.418896 4.161692 1.619528 5.432024
139 -0.460299 4.167824 3.929916 1.547523 3.264638
140 -47.850762 -86.743132 -38.270049 -24.007733 -15.975007
141 81.660621 748.490946 73.124043 47.306196 27.458380
142 8.320391 2.916765 4.643963 3.078330 3.857541
9-10_girls 7-8_girls 6&U_girls Season_1_Compared_girls \
0 -9.034268 -2.272727 -10.236220 2011.0
1 6.849315 15.348837 66.666667 2012.0
2 13.141026 1.209677 -21.052632 2013.0
3 19.546742 11.952191 18.666667 2014.0
4 -4.502370 29.893238 64.606742 2015.0
.. ... ... ... ...
138 7.880384 4.669587 3.082624 2017.0
139 2.824268 -0.026714 1.617779 2018.0
140 -12.851814 -4.551528 -22.272252 2019.0
141 21.410506 13.503173 34.188476 2020.0
142 2.684080 5.179643 -2.821462 2021.0
Season_2_Compared_girls
0 2012.0
1 2013.0
2 2014.0
3 2015.0
4 2016.0
.. ...
138 2018.0
139 2019.0
140 2020.0
141 2021.0
142 2022.0
[143 rows x 24 columns]
# Define the age groups
age_groups = ['All_Ages', '19&over', '17-18', '15-16', '13-14', '11-12', '9-10', '7-8', '6&U']
# Create an empty list to store dictionaries
lower_growth_info_list = []
# Iterate through age groups
for age_group in age_groups:
# Extract growth values for boys and girls
boys_growth = boys_district_growth[age_group]
girls_growth = girls_district_growth[age_group]
# Find where girls show lower growth than boys
lower_growth_mask = girls_growth < boys_growth
# Extract relevant information and append to the list
for i in range(len(boys_growth[lower_growth_mask])):
lower_growth_info_list.append({
'Season_Start': boys_district_growth['Season_Start'][lower_growth_mask].iloc[i],
'District': boys_district_growth['District'][lower_growth_mask].iloc[i],
'Age_Group': age_group,
'Lower_Growth_Percentage': (boys_growth - girls_growth)[lower_growth_mask].iloc[i]
})
# Create DataFrame from the list
lower_growth_info = pd.DataFrame(lower_growth_info_list)
# Display the results
lower_growth_info.head()
| Season_Start | District | Age_Group | Lower_Growth_Percentage | |
|---|---|---|---|---|
| 0 | 2012.0 | 1 | All_Ages | 3.558536 |
| 1 | 2020.0 | 1 | All_Ages | 1.913054 |
| 2 | 2012.0 | 2 | All_Ages | 2.731824 |
| 3 | 2013.0 | 2 | All_Ages | 2.588978 |
| 4 | 2020.0 | 2 | All_Ages | 0.721547 |
# Assuming lower_growth_info is the DataFrame containing the information
most_common_district = lower_growth_info['District'].value_counts().idxmax()
most_common_age_group = lower_growth_info['Age_Group'].value_counts().idxmax()
print("Most Common District:", most_common_district)
print("Most Common Age Group:", most_common_age_group)
Most Common District: 9 Most Common Age Group: 19&over
# Assuming lower_growth_info is the DataFrame containing the information
most_common_combination = lower_growth_info.groupby(['District', 'Age_Group']).size().idxmax()
print("Most Common Combination (District, Age Group):", most_common_combination)
Most Common Combination (District, Age Group): (7, '17-18')
# Assuming lower_growth_info is the DataFrame containing the information
combination_counts = lower_growth_info.groupby(['District', 'Age_Group']).size().reset_index(name='Combination_Count')
sorted_combination_counts = combination_counts.sort_values(by='Combination_Count', ascending=False)
print("Sorted Combination Counts:")
print(sorted_combination_counts)
Sorted Combination Counts:
District Age_Group Combination_Count
56 7 17-18 8
72 9 13-14 8
57 7 19&over 8
39 5 19&over 7
73 9 15-16 7
.. ... ... ...
106 12 All_Ages 1
49 6 6&U 1
61 7 All_Ages 1
6 1 7-8 1
115 13 All_Ages 1
[116 rows x 3 columns]
# Plotting growth for each age group
age_groups = ['All_Ages', '19&over', '17-18', '15-16', '13-14', '11-12', '9-10', '7-8', '6&U']
for age_group in age_groups:
plt.figure(figsize=(10, 6))
plt.plot(combined_growth['Season_Start'], combined_growth[f'{age_group}_boys'], label=f'Boys - {age_group}')
plt.plot(combined_growth['Season_Start'], combined_growth[f'{age_group}_girls'], label=f'Girls - {age_group}')
plt.xlabel('Season Start Year')
plt.ylabel('Growth (%)')
plt.title(f'Comparison of Growth for {age_group} between Boys and Girls Districts')
plt.legend()
plt.grid(True)
plt.show()
# Assuming boys_district_growth and girls_district_growth DataFrames
age_groups = ['All_Ages', '19&over', '17-18', '15-16', '13-14', '11-12', '9-10', '7-8', '6&U']
for age_group in age_groups:
boys_growth = boys_district_growth[age_group]
girls_growth = girls_district_growth[age_group]
plt.figure(figsize=(10, 6))
plt.bar(boys_growth.index - 0.2, boys_growth, width=0.4, label='Boys', align='center')
plt.bar(girls_growth.index + 0.2, girls_growth, width=0.4, label='Girls', align='center')
plt.title(f'Growth Comparison for {age_group}')
plt.xlabel('District')
plt.ylabel('Growth')
plt.legend()
plt.show()
# Columns to include in the plot
age_groups = ['All_Ages', '19&over', '17-18', '15-16', '13-14', '11-12', '9-10', '7-8', '6&U']
# Bar width
bar_width = 0.35
# Positions for each age group on X-axis
index = np.arange(len(age_groups))
# Make the graph wider
fig, ax = plt.subplots(figsize=(12, 6))
# Bar graph for boys
ax.bar(index, growth_boys_district_13[age_groups].iloc[0], bar_width, label='Boys')
# Bar graph for girls
ax.bar(index + bar_width, growth_girls_district_13[age_groups].iloc[0], bar_width, label='Girls')
# Labels and title
ax.set_xlabel('Age Groups')
ax.set_ylabel('Growth (%)')
ax.set_title('Growth Comparison between Boys and Girls in District 13')
ax.set_xticks(index + bar_width / 2)
ax.set_xticklabels(age_groups)
# Place the legend above the bars
ax.legend(loc='upper center', bbox_to_anchor=(0.5, 1.15), ncol=2)
plt.show()
# Selecting relevant columns from all_state and girls_state
all_state_subset = all_state[['Season_Start', 'District', 'State', 'All_Ages', '19&over', '17-18', '15-16', '13-14', '11-12', '9-10', '7-8', '6&U']]
girls_state_subset = girls_state[['Season_Start', 'District', 'State', 'All_Ages', '19&over', '17-18', '15-16', '13-14', '11-12', '9-10', '7-8', '6&U']]
# Subtracting girls_district values from all_district values
boys_state = all_state_subset.copy()
boys_state[['All_Ages', '19&over', '17-18', '15-16', '13-14', '11-12', '9-10', '7-8', '6&U']] -= girls_state_subset[['All_Ages', '19&over', '17-18', '15-16', '13-14', '11-12', '9-10', '7-8', '6&U']]
# Displaying the resulting DataFrame (boys_df)
boys_state.head()
| Season_Start | District | State | All_Ages | 19&over | 17-18 | 15-16 | 13-14 | 11-12 | 9-10 | 7-8 | 6&U | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2011.0 | 1 | 1.1 | 987.0 | 308.0 | 126.0 | 144.0 | 109.0 | 111.0 | 97.0 | 70.0 | 22.0 |
| 1 | 2011.0 | 1 | 1.2 | 15781.0 | 6091.0 | 1488.0 | 1740.0 | 1688.0 | 1727.0 | 1387.0 | 1088.0 | 572.0 |
| 2 | 2011.0 | 1 | 1.3 | 16625.0 | 4302.0 | 1431.0 | 1955.0 | 2094.0 | 2403.0 | 2098.0 | 1547.0 | 795.0 |
| 3 | 2011.0 | 2 | 2.1 | 23927.0 | 6121.0 | 1577.0 | 2293.0 | 2558.0 | 2966.0 | 3453.0 | 2793.0 | 2166.0 |
| 4 | 2011.0 | 2 | 2.2 | 3208.0 | 958.0 | 154.0 | 236.0 | 276.0 | 347.0 | 362.0 | 414.0 | 461.0 |
def calculate_season_to_season_growth(df):
"""
Calculates season-to-season growth for each state in the given DataFrame.
Parameters:
- df: DataFrame with columns 'Season_Start', 'State', and numeric columns for age groups.
Returns:
- growth_df: DataFrame with season-to-season growth for each state including the seasons being compared.
"""
# Sort DataFrame by 'Season_Start' for chronological order
df = df.sort_values(by=['State', 'Season_Start'])
# Group by 'State' and calculate growth
growth_df = pd.DataFrame()
for state, group_df in df.groupby('State'):
# Calculate growth for numeric columns
growth = group_df.set_index('Season_Start').pct_change().dropna() * 100
growth['State'] = state
growth.reset_index(inplace=True)
# Add columns for the seasons being compared
growth['Season_1_Compared'] = growth['Season_Start'].shift(1)
growth['Season_2_Compared'] = growth['Season_Start']
# Append the calculated growth DataFrame to the result DataFrame
growth_df = pd.concat([growth_df, growth], ignore_index=True)
return growth_df
# Example usage:
# Assuming df is your DataFrame with columns 'Season_Start', 'State', and numeric columns for age groups.
# growth_result = calculate_season_to_season_growth(df)
boys_growth_state = calculate_season_to_season_growth(boys_state)
girls_growth_state = calculate_season_to_season_growth(girls_state)
boys_growth_state.head()
| Season_Start | District | State | All_Ages | 19&over | 17-18 | 15-16 | 13-14 | 11-12 | 9-10 | 7-8 | 6&U | Season_1_Compared | Season_2_Compared | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2012.0 | 0.0 | 1.1 | -15.400203 | -6.168831 | -26.984127 | -23.611111 | -11.926606 | -18.018018 | -14.432990 | -21.428571 | -13.636364 | NaN | 2012.0 |
| 1 | 2013.0 | 0.0 | 1.1 | 1.077844 | -5.536332 | 5.434783 | -1.818182 | 16.666667 | 10.989011 | -7.228916 | -10.909091 | 42.105263 | 2012.0 | 2013.0 |
| 2 | 2014.0 | 0.0 | 1.1 | 4.502370 | 4.029304 | 10.309278 | 0.925926 | 0.000000 | 4.950495 | -6.493506 | 22.448980 | 18.518519 | 2013.0 | 2014.0 |
| 3 | 2015.0 | 0.0 | 1.1 | -1.587302 | 1.760563 | -25.233645 | 1.834862 | 0.000000 | 0.000000 | 9.722222 | 0.000000 | -3.125000 | 2014.0 | 2015.0 |
| 4 | 2016.0 | 0.0 | 1.1 | 2.073733 | -5.882353 | 11.250000 | -6.306306 | 4.464286 | -17.924528 | -1.265823 | 36.666667 | 83.870968 | 2015.0 | 2016.0 |
boys_growth_state['Season_1_Compared'].fillna(2011, inplace=True)
girls_growth_state['Season_1_Compared'].fillna(2011, inplace=True)
girls_growth_state.head()
| Season_Start | District | State | All_Ages | 19&over | 17-18 | 15-16 | 13-14 | 11-12 | 9-10 | 7-8 | 6&U | Season_1_Compared | Season_2_Compared | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2012.0 | 0.0 | 1.1 | -10.344828 | -18.032787 | -66.666667 | 0.000000 | 500.000000 | 25.000000 | -33.333333 | 100.000000 | 100.0 | 2011.0 | 2012.0 |
| 1 | 2013.0 | 0.0 | 1.1 | -8.974359 | -18.000000 | 100.000000 | -83.333333 | 0.000000 | -40.000000 | 150.000000 | 0.000000 | 150.0 | 2012.0 | 2013.0 |
| 2 | 2014.0 | 0.0 | 1.1 | 12.676056 | 9.756098 | 0.000000 | 300.000000 | 16.666667 | 33.333333 | 80.000000 | 0.000000 | -80.0 | 2013.0 | 2014.0 |
| 3 | 2015.0 | 0.0 | 1.1 | 1.250000 | -8.888889 | -83.333333 | 125.000000 | -14.285714 | 75.000000 | -33.333333 | 75.000000 | 300.0 | 2014.0 | 2015.0 |
| 4 | 2016.0 | 0.0 | 1.1 | 32.098765 | -2.439024 | 700.000000 | -22.222222 | 83.333333 | 42.857143 | 50.000000 | 14.285714 | 250.0 | 2015.0 | 2016.0 |
def calculate_2011_2022_growth(dataframe):
# Filter data for Season_Start 2011 and 2022
season_2011 = dataframe[dataframe['Season_Start'] == 2011]
season_2022 = dataframe[dataframe['Season_Start'] == 2022]
# Merge dataframes on State to get corresponding values
merged_data = pd.merge(season_2011, season_2022, on='State', suffixes=('_2011', '_2022'))
# Initialize a new DataFrame for growth
growth_data = pd.DataFrame()
growth_data['State'] = merged_data['State']
# Calculate growth for each age group
for age_group in ['All_Ages', '19&over', '17-18', '15-16', '13-14', '11-12', '9-10', '7-8', '6&U']:
numerator = merged_data[f'{age_group}_2022'] - merged_data[f'{age_group}_2011']
denominator = merged_data[f'{age_group}_2011']
# Replace zero values in the denominator with 1
denominator = np.where(denominator == 0, 1, denominator)
# Calculate growth percentage and store in the new dataframe
growth_data[age_group] = (numerator / denominator) * 100
return growth_data
# Example usage:
# Assuming your dataframe is named 'df'
# growth_result = calculate_growth(df)
# print(growth_result)
boys_growth_result = calculate_2011_2022_growth(boys_state)
boys_growth_result.head()
| State | All_Ages | 19&over | 17-18 | 15-16 | 13-14 | 11-12 | 9-10 | 7-8 | 6&U | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1.1 | -18.034448 | -19.480519 | -51.587302 | -35.416667 | -12.844037 | -3.603604 | -11.340206 | 15.714286 | 72.727273 |
| 1 | 1.2 | -11.596223 | -11.771466 | -19.758065 | -15.287356 | -7.523697 | -15.923567 | -7.642394 | -0.643382 | -6.643357 |
| 2 | 1.3 | 0.000000 | 12.505811 | -16.911251 | -11.304348 | -8.213945 | -23.179359 | -10.295520 | 20.620556 | 69.308176 |
| 3 | 2.1 | -6.745518 | -6.747264 | 24.857324 | 20.191888 | 18.803753 | -8.226568 | -23.718506 | -31.185106 | -27.839335 |
| 4 | 2.2 | -1.527431 | -22.442589 | 18.181818 | 30.084746 | 41.666667 | 16.426513 | 15.469613 | -10.628019 | -25.379610 |
girls_growth_result = calculate_2011_2022_growth(girls_state)
girls_growth_result.head()
| State | All_Ages | 19&over | 17-18 | 15-16 | 13-14 | 11-12 | 9-10 | 7-8 | 6&U | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1.1 | 42.528736 | -50.819672 | -33.333333 | 66.666667 | 1400.000000 | 400.000000 | 533.333333 | 400.000000 | 1300.000000 |
| 1 | 1.2 | 26.861702 | -2.443281 | 36.567164 | 34.239130 | 26.737968 | 29.775281 | 70.312500 | 94.805195 | 93.023256 |
| 2 | 1.3 | 88.107203 | 46.747967 | 109.210526 | 114.782609 | 72.666667 | 53.886010 | 78.421053 | 152.482270 | 174.698795 |
| 3 | 2.1 | 26.779394 | 36.148649 | 41.666667 | 35.714286 | 31.944444 | 25.671642 | 19.726027 | 10.231023 | 10.833333 |
| 4 | 2.2 | 83.534137 | 17.647059 | -45.454545 | 44.000000 | 154.166667 | 100.000000 | 131.250000 | 208.333333 | 95.454545 |
print(girls_growth_state.columns)
Index(['Season_Start', 'District', 'State', 'All_Ages', '19&over', '17-18',
'15-16', '13-14', '11-12', '9-10', '7-8', '6&U', 'Season_1_Compared',
'Season_2_Compared'],
dtype='object')
print(boys_growth_state.columns)
Index(['Season_Start', 'District', 'State', 'All_Ages', '19&over', '17-18',
'15-16', '13-14', '11-12', '9-10', '7-8', '6&U', 'Season_1_Compared',
'Season_2_Compared'],
dtype='object')
import pandas as pd
from scipy.stats import ttest_ind
# Assuming boys_growth_state and girls_growth_state are your DataFrames
age_groups = ['All_Ages', '19&over', '17-18', '15-16', '13-14', '11-12', '9-10', '7-8', '6&U']
for age_group in age_groups:
# Check if the column exists in boys_growth_state DataFrame
if age_group in boys_growth_state.columns:
boys_growth_state_age_group = boys_growth_state[age_group].dropna()
girls_growth_state_age_group = girls_growth_state[age_group].dropna()
# Summary Statistics for Boys
boys_summary = boys_growth_state_age_group.describe()
# Summary Statistics for Girls
girls_summary = girls_growth_state_age_group.describe()
# T-test
t_statistic, p_value = ttest_ind(boys_growth_state_age_group, girls_growth_state_age_group, equal_var=False)
print(f"\nSummary Statistics for {age_group} - Boys:")
print(boys_summary)
print(f"\nSummary Statistics for {age_group} - Girls:")
print(girls_summary)
print(f"\nT-test results for {age_group}:")
print(f"T-statistic: {t_statistic}")
print(f"P-value: {p_value}")
# Check for statistical significance
if p_value < 0.05:
print("The difference is statistically significant.")
else:
print("The difference is not statistically significant.")
else:
print(f"Column '{age_group}' not found in boys_growth_state DataFrame.")
Summary Statistics for All_Ages - Boys: count 577.000000 mean 3.665880 std 43.366235 min -95.625000 25% -2.258852 50% 1.077844 75% 5.009185 max 979.166667 Name: All_Ages, dtype: float64 Summary Statistics for All_Ages - Girls: count 556.000000 mean 5.941174 std 17.175375 min -60.169492 25% -0.737704 50% 3.907092 75% 9.583448 max 117.021277 Name: All_Ages, dtype: float64 T-test results for All_Ages: T-statistic: -1.1687573290849314 P-value: 0.24286887218525535 The difference is not statistically significant. Summary Statistics for 19&over - Boys: count 577.000000 mean 6.447437 std 56.443760 min -95.319149 25% -4.685408 50% 1.644962 75% 7.785825 max 1062.500000 Name: 19&over, dtype: float64 Summary Statistics for 19&over - Girls: count 556.000000 mean 8.623032 std 40.068148 min -79.762913 25% -4.599567 50% 2.186837 75% 11.764706 max 344.769874 Name: 19&over, dtype: float64 T-test results for 19&over: T-statistic: -0.7502500904053564 P-value: 0.453273689606679 The difference is not statistically significant. Summary Statistics for 17-18 - Boys: count 577.000000 mean inf std NaN min -100.000000 25% -6.146572 50% 0.000000 75% 7.430341 max inf Name: 17-18, dtype: float64 Summary Statistics for 17-18 - Girls: count 556.000000 mean inf std NaN min -100.000000 25% -11.111111 50% 1.712529 75% 24.431818 max inf Name: 17-18, dtype: float64 T-test results for 17-18: T-statistic: nan P-value: nan The difference is not statistically significant. Summary Statistics for 15-16 - Boys: count 577.000000 mean inf std NaN min -100.000000 25% -3.636364 50% 1.358025 75% 6.382979 max inf Name: 15-16, dtype: float64 Summary Statistics for 15-16 - Girls: count 556.000000 mean inf std NaN min -100.000000 25% -8.163410 50% 4.650141 75% 19.211666 max inf Name: 15-16, dtype: float64 T-test results for 15-16: T-statistic: nan P-value: nan The difference is not statistically significant. Summary Statistics for 13-14 - Boys: count 577.000000 mean 2.763956 std 18.933912 min -90.000000 25% -2.762431 50% 0.673077 75% 6.008584 max 250.000000 Name: 13-14, dtype: float64 Summary Statistics for 13-14 - Girls: count 556.000000 mean inf std NaN min -100.000000 25% -4.545455 50% 3.554203 75% 15.023585 max inf Name: 13-14, dtype: float64 T-test results for 13-14: T-statistic: nan P-value: nan The difference is not statistically significant. Summary Statistics for 11-12 - Boys: count 577.000000 mean 4.345506 std 50.389936 min -100.000000 25% -3.303571 50% 0.500626 75% 6.071429 max 1100.000000 Name: 11-12, dtype: float64 Summary Statistics for 11-12 - Girls: count 556.000000 mean 9.430226 std 29.643914 min -75.000000 25% -3.508786 50% 5.250033 75% 16.666667 max 300.000000 Name: 11-12, dtype: float64 T-test results for 11-12: T-statistic: -2.0791041667980226 P-value: 0.03787888769548562 The difference is statistically significant. Summary Statistics for 9-10 - Boys: count 577.000000 mean 6.049937 std 81.721214 min -94.444444 25% -4.070289 50% 1.025641 75% 6.959707 max 1900.000000 Name: 9-10, dtype: float64 Summary Statistics for 9-10 - Girls: count 556.000000 mean 9.963006 std 33.568354 min -100.000000 25% -5.882353 50% 3.957140 75% 17.722474 max 255.555556 Name: 9-10, dtype: float64 T-test results for 9-10: T-statistic: -1.0610426791638377 P-value: 0.2890028249113801 The difference is not statistically significant. Summary Statistics for 7-8 - Boys: count 577.000000 mean inf std NaN min -100.000000 25% -6.140351 50% 1.549681 75% 9.308511 max inf Name: 7-8, dtype: float64 Summary Statistics for 7-8 - Girls: count 556.000000 mean 13.927815 std 44.660714 min -75.000000 25% -6.079265 50% 5.970822 75% 22.435897 max 300.000000 Name: 7-8, dtype: float64 T-test results for 7-8: T-statistic: nan P-value: nan The difference is not statistically significant. Summary Statistics for 6&U - Boys: count 577.000000 mean inf std NaN min -93.750000 25% -7.899136 50% 0.212314 75% 11.111111 max inf Name: 6&U, dtype: float64 Summary Statistics for 6&U - Girls: count 556.000000 mean inf std NaN min -100.000000 25% -9.104767 50% 4.517671 75% 23.543285 max inf Name: 6&U, dtype: float64 T-test results for 6&U: T-statistic: nan P-value: nan The difference is not statistically significant.
E:\Anaconda\Lib\site-packages\scipy\stats\_stats_py.py:1070: RuntimeWarning: invalid value encountered in subtract a_zero_mean = a - mean E:\Anaconda\Lib\site-packages\scipy\stats\_stats_py.py:6999: RuntimeWarning: invalid value encountered in scalar subtract d = mean1 - mean2 E:\Anaconda\Lib\site-packages\scipy\stats\_stats_py.py:7474: RuntimeWarning: invalid value encountered in scalar subtract estimate = m1-m2 E:\Anaconda\Lib\site-packages\scipy\stats\_stats_py.py:1070: RuntimeWarning: invalid value encountered in subtract a_zero_mean = a - mean E:\Anaconda\Lib\site-packages\scipy\stats\_stats_py.py:6999: RuntimeWarning: invalid value encountered in scalar subtract d = mean1 - mean2 E:\Anaconda\Lib\site-packages\scipy\stats\_stats_py.py:7474: RuntimeWarning: invalid value encountered in scalar subtract estimate = m1-m2 E:\Anaconda\Lib\site-packages\scipy\stats\_stats_py.py:1070: RuntimeWarning: invalid value encountered in subtract a_zero_mean = a - mean E:\Anaconda\Lib\site-packages\scipy\stats\_stats_py.py:1070: RuntimeWarning: invalid value encountered in subtract a_zero_mean = a - mean E:\Anaconda\Lib\site-packages\scipy\stats\_stats_py.py:1070: RuntimeWarning: invalid value encountered in subtract a_zero_mean = a - mean E:\Anaconda\Lib\site-packages\scipy\stats\_stats_py.py:6999: RuntimeWarning: invalid value encountered in scalar subtract d = mean1 - mean2 E:\Anaconda\Lib\site-packages\scipy\stats\_stats_py.py:7474: RuntimeWarning: invalid value encountered in scalar subtract estimate = m1-m2
# Combine the boys and girls growth DataFrames
combined_growth_state = pd.merge(boys_growth_state, girls_growth_state, on=['Season_Start', 'District', 'State'], suffixes=('_boys', '_girls'))
# Display the combined growth DataFrame
print(combined_growth_state)
Season_Start District State All_Ages_boys 19&over_boys 17-18_boys \
0 2012.0 0.0 1.1 -15.400203 -6.168831 -26.984127
1 2013.0 0.0 1.1 1.077844 -5.536332 5.434783
2 2014.0 0.0 1.1 4.502370 4.029304 10.309278
3 2015.0 0.0 1.1 -1.587302 1.760563 -25.233645
4 2016.0 0.0 1.1 2.073733 -5.882353 11.250000
.. ... ... ... ... ... ...
551 2018.0 0.0 13.0 0.478469 0.860727 1.320340
552 2019.0 0.0 13.0 -1.546485 -2.231212 -0.868756
553 2020.0 0.0 13.0 -19.717419 -37.004616 -3.818466
554 2021.0 0.0 13.0 19.829016 45.303172 8.741458
555 2022.0 0.0 13.0 1.191404 2.192970 -0.665844
15-16_boys 13-14_boys 11-12_boys 9-10_boys ... 19&over_girls \
0 -23.611111 -11.926606 -18.018018 -14.432990 ... -18.032787
1 -1.818182 16.666667 10.989011 -7.228916 ... -18.000000
2 0.925926 0.000000 4.950495 -6.493506 ... 9.756098
3 1.834862 0.000000 0.000000 9.722222 ... -8.888889
4 -6.306306 4.464286 -17.924528 -1.265823 ... -2.439024
.. ... ... ... ... ... ...
551 0.702840 -1.496491 0.254185 1.358171 ... 4.060527
552 0.747415 -1.027535 -0.815085 -1.131288 ... -0.460299
553 -1.098589 -3.558683 -7.009771 -11.610813 ... -45.239096
554 1.868744 2.945691 4.681328 10.724984 ... 72.996833
555 1.955049 0.067399 0.299558 -0.491875 ... 8.320391
17-18_girls 15-16_girls 13-14_girls 11-12_girls 9-10_girls \
0 -66.666667 0.000000 500.000000 25.000000 -33.333333
1 100.000000 -83.333333 0.000000 -40.000000 150.000000
2 0.000000 300.000000 16.666667 33.333333 80.000000
3 -83.333333 125.000000 -14.285714 75.000000 -33.333333
4 700.000000 -22.222222 83.333333 42.857143 50.000000
.. ... ... ... ... ...
551 2.418896 4.161692 1.619528 5.432024 7.880384
552 4.167824 3.929916 1.547523 3.264638 2.824268
553 4.507869 5.278084 2.467872 -4.131307 -9.155646
554 7.631445 1.511523 9.245283 11.712062 16.470698
555 2.916765 4.643963 3.078330 3.857541 2.684080
7-8_girls 6&U_girls Season_1_Compared_girls Season_2_Compared_girls
0 100.000000 100.000000 2011.0 2012.0
1 0.000000 150.000000 2012.0 2013.0
2 0.000000 -80.000000 2013.0 2014.0
3 75.000000 300.000000 2014.0 2015.0
4 14.285714 250.000000 2015.0 2016.0
.. ... ... ... ...
551 4.669587 3.082624 2017.0 2018.0
552 -0.026714 1.617779 2018.0 2019.0
553 -13.895074 -21.974753 2019.0 2020.0
554 25.819799 33.676834 2020.0 2021.0
555 5.179643 -2.821462 2021.0 2022.0
[556 rows x 25 columns]
import pandas as pd
# Assuming girls_growth_state and boys_growth_state are your DataFrames
# Align indices of both DataFrames
girls_growth_state, boys_growth_state = girls_growth_state.align(boys_growth_state, join='inner')
# Find where girls show lower growth than boys for all columns
lower_growth_mask = girls_growth_state < boys_growth_state
# Create an empty list to store the results
result_list = []
# Iterate through all columns and extract relevant information
for age_group in girls_growth_state.columns:
# Exclude 'Season_Start' and 'District' columns from comparison
if age_group in ['Season_Start', 'District', 'Season_1_Compared', 'Season_2_Compared']:
continue
# Extract relevant information and append to the result list
for index, row in girls_growth_state[lower_growth_mask[age_group]].iterrows():
season_start = index
state = row['State'] # Assuming 'State' is a column in your DataFrame
girl_growth = row[age_group]
boy_growth = boys_growth_state.loc[index, age_group]
result_list.append({
'Season_Start': season_start,
'State': state,
'Age_Group': age_group,
'Girl_Growth': girl_growth,
'Boy_Growth': boy_growth
})
# Create the result DataFrame
lower_growth_info_state = pd.DataFrame(result_list)
# Display the result DataFrame
print(lower_growth_info_state)
Season_Start State Age_Group Girl_Growth Boy_Growth 0 1 1.1 All_Ages -8.974359 1.077844 1 6 1.1 All_Ages -6.956522 -3.257329 2 8 1.1 All_Ages -25.892857 -15.320665 3 11 1.2 All_Ages -5.186170 0.297827 4 15 1.2 All_Ages -2.110977 3.077528 ... ... ... ... ... ... 2064 547 13.0 6&U 3.610808 6.161137 2065 548 13.0 6&U 13.320080 50.000000 2066 549 13.0 6&U 13.498452 42.857143 2067 553 13.0 6&U -21.974753 12.406015 2068 555 13.0 6&U -2.821462 11.851852 [2069 rows x 5 columns]
# Assuming lower_growth_info is the DataFrame containing the information
most_common_state = lower_growth_info_state['State'].value_counts().idxmax()
most_common_age_group_state = lower_growth_info_state['Age_Group'].value_counts().idxmax()
print("Most Common State:", most_common_state)
print("Most Common Age Group:", most_common_age_group_state)
Most Common State: 10.5 Most Common Age Group: 19&over
# Assuming lower_growth_info is the DataFrame containing the information
most_common_combination_state = lower_growth_info_state.groupby(['State', 'Age_Group']).size().idxmax()
print("Most Common Combination (State, Age Group):", most_common_combination_state)
Most Common Combination (State, Age Group): (5.2, '15-16')
# Assuming lower_growth_info is the DataFrame containing the information
combination_counts_state = lower_growth_info_state.groupby(['State', 'Age_Group']).size().reset_index(name='Combination_Count')
sorted_combination_counts_state = combination_counts_state.sort_values(by='Combination_Count', ascending=False)
print("Sorted Combination Counts:")
print(sorted_combination_counts_state)
Sorted Combination Counts:
State Age_Group Combination_Count
181 7.4 17-18 8
108 5.2 15-16 8
225 9.3 15-16 8
344 11.7 19&over 8
129 5.4 6&U 8
.. ... ... ...
0 1.1 11-12 2
105 5.1 All_Ages 1
147 6.0 6&U 1
149 6.0 9-10 1
34 2.1 9-10 1
[466 rows x 3 columns]
# Plotting growth for each age group
age_groups = ['All_Ages', '19&over', '17-18', '15-16', '13-14', '11-12', '9-10', '7-8', '6&U']
for age_group in age_groups:
plt.figure(figsize=(10, 6))
plt.plot(combined_growth_state['Season_Start'], combined_growth_state[f'{age_group}_boys'], label=f'Boys - {age_group}')
plt.plot(combined_growth_state['Season_Start'], combined_growth_state[f'{age_group}_girls'], label=f'Girls - {age_group}')
plt.xlabel('Season Start Year')
plt.ylabel('Growth (%)')
plt.title(f'Comparison of Growth for {age_group} between Boys and Girls States')
plt.legend()
plt.grid(True)
plt.show()
import matplotlib.pyplot as plt
# Assuming boys_growth_state and girls_growth_state are your DataFrames
# Align indices of both DataFrames
boys_growth_state, girls_growth_state = boys_growth_state.align(girls_growth_state, join='inner')
# Plot growth for each age group
for age_group in boys_growth_state.columns:
# Exclude 'Season_Start', 'District', 'State' columns from comparison
if age_group in ('Season_Start', 'District', 'State', 'Season_1_Compared', 'Season_2_Compared'):
continue
# Plot boys' and girls' growth for the age group
plt.figure(figsize=(10, 6))
plt.plot(boys_growth_state['Season_Start'], boys_growth_state[age_group], label='Boys')
plt.plot(girls_growth_state['Season_Start'], girls_growth_state[age_group], label='Girls')
plt.title(f'Growth Comparison - {age_group}')
plt.xlabel('Season Start')
plt.ylabel('Growth')
plt.legend()
plt.show()
import matplotlib.pyplot as plt
# Assuming boys_growth_state and girls_growth_state are your DataFrames
# Align indices of both DataFrames
boys_growth_state, girls_growth_state = boys_growth_state.align(girls_growth_state, join='inner')
# Specify age groups
age_groups = ['All_Ages', '19&over', '17-18', '15-16', '13-14', '11-12', '9-10', '7-8', '6&U']
# Plot growth for each age group
for age_group in age_groups:
# Filter DataFrame for the specified age group
boys_growth_state_age_group = boys_growth_state[[age_group]].dropna()
girls_growth_state_age_group = girls_growth_state[[age_group]].dropna()
# Plot boys' and girls' growth for the age group
plt.figure(figsize=(10, 6))
plt.plot(boys_growth_state_age_group, label='Boys')
plt.plot(girls_growth_state_age_group, label='Girls')
plt.title(f'Growth Comparison - {age_group}')
plt.xlabel('Season Start')
plt.ylabel('Growth')
plt.legend()
plt.show()
# Columns to include in the plot
age_groups = ['All_Ages', '19&over', '17-18', '15-16', '13-14', '11-12', '9-10', '7-8', '6&U']
# Bar width
bar_width = 0.35
# Positions for each age group on X-axis
index = np.arange(len(age_groups))
# Make the graph wider
fig, ax = plt.subplots(figsize=(12, 6))
# Bar graph for boys
ax.bar(index, boys_growth_result[age_groups].iloc[0], bar_width, label='Boys')
# Bar graph for girls
ax.bar(index + bar_width, girls_growth_result[age_groups].iloc[0], bar_width, label='Girls')
# Labels and title
ax.set_xlabel('Age Groups')
ax.set_ylabel('Growth (%)')
ax.set_title('Growth Comparison between Boys and Girls in States, 2011-2022')
ax.set_xticks(index + bar_width / 2)
ax.set_xticklabels(age_groups)
# Place the legend above the bars
ax.legend(loc='upper center', bbox_to_anchor=(0.5, 1.15), ncol=2)
plt.show()
from sklearn.linear_model import LinearRegression
import numpy as np
# Assuming boys_growth_state and girls_growth_state are your DataFrames
# Align indices of both DataFrames
boys_growth_state, girls_growth_state = boys_growth_state.align(girls_growth_state, join='inner')
# Iterate over each age group column
for age_group in boys_growth_state.columns:
# Exclude 'Season_Start', 'District', 'State' columns from comparison
if age_group in ('Season_Start', 'District', 'State'):
continue
# Extract x and y values for regression
x = boys_growth_state['Season_Start'].values.reshape(-1, 1)
y_boys = boys_growth_state[age_group].values.reshape(-1, 1)
y_girls = girls_growth_state[age_group].values.reshape(-1, 1)
# Remove NaN, infinity, and very large values
mask_boys = np.isfinite(y_boys) & (y_boys < 1e10)
mask_girls = np.isfinite(y_girls) & (y_girls < 1e10)
x = x[mask_boys & mask_girls].reshape(-1, 1)
y_boys = y_boys[mask_boys & mask_girls].reshape(-1, 1)
y_girls = y_girls[mask_boys & mask_girls].reshape(-1, 1)
# Fit linear regression models
model_boys = LinearRegression().fit(x, y_boys)
model_girls = LinearRegression().fit(x, y_girls)
# Predict y values using the models
y_pred_boys = model_boys.predict(x)
y_pred_girls = model_girls.predict(x)
# Print boys' and girls' growth for the age group
print(f'Age Group: {age_group}')
print('Boys Growth:')
print('Actual:', y_boys.flatten())
print('Predicted:', y_pred_boys.flatten())
print('\nGirls Growth:')
print('Actual:', y_girls.flatten())
print('Predicted:', y_pred_girls.flatten())
print('\n')
Age Group: All_Ages Boys Growth: Actual: [-1.54002026e+01 1.07784431e+00 4.50236967e+00 -1.58730159e+00 2.07373272e+00 3.95033860e+00 -3.25732899e+00 -5.49943883e+00 -1.53206651e+01 1.69705470e+01 -2.99760192e+00 2.97826500e-01 1.85746778e+00 -3.24401439e+00 -2.09628822e+00 3.07752750e+00 -1.10532334e+00 -1.86279548e-01 -5.51515542e+00 -1.40512192e+01 1.31230684e+01 -2.26970228e+00 2.58646617e+00 3.92846673e-01 1.10384301e+00 1.19577147e+00 2.84279027e+00 -1.49311723e+00 3.91052009e+00 -5.43354482e+00 -2.16182121e+01 1.81139805e+01 2.97305667e+00 4.67254566e+00 8.68037532e+00 1.81490870e+00 3.03106845e+00 -2.66171681e-01 2.55996067e+00 -6.31377114e+00 -2.81412177e+00 -2.18712395e+01 1.15325375e+01 -3.70706025e+00 -5.57980050e+00 2.17893694e+00 7.72213247e+00 -1.13977205e+00 2.03276699e+00 3.86559619e-01 -6.63507109e+00 -6.09137056e+00 -2.77027027e+00 1.25781793e+01 -2.50000000e+00 -2.17025749e+01 3.89261745e+00 -6.45994832e-01 -5.91677503e+00 6.21976503e+00 1.69160703e+00 4.41458733e+00 1.10294118e+00 -9.69696970e+00 8.25503356e+00 4.15375077e+00 2.31343284e+00 -2.69876003e+00 -2.21889055e+00 1.37994480e+00 6.53357532e+00 -2.99545713e+00 2.76598858e+00 2.72002279e+01 -1.08262427e+01 1.86315129e+01 6.79436977e+00 -5.51989730e+00 5.70652174e+00 6.55526992e+00 5.48854041e+00 2.22984563e+00 -2.12527964e+00 -2.00000000e+00 1.34110787e+00 -3.73993096e+00 1.32098027e+01 -2.11193242e-01 -1.67191997e+00 2.18815331e+00 1.09110747e+00 2.92768484e+00 3.15244462e+00 1.42321621e+00 9.64730940e-01 -2.53769312e+00 -7.72217978e+00 1.11072784e+01 9.99689537e-01 8.05931657e-02 2.69501262e+00 2.67395055e+00 -1.83549299e+00 2.20435685e-01 -2.39358261e+00 -9.49098621e-01 -1.20978534e+00 -1.28420482e+01 1.03947777e+01 -2.84958045e+00 -2.15834782e+00 -2.76855396e+00 -3.56738885e-01 -1.68092910e+00 -8.21528487e-02 -2.89549121e+00 -6.86530276e-01 -4.47946910e+00 -3.46625175e+01 1.85600886e+01 -1.39200299e+00 6.24630542e+00 1.89169139e+00 3.54932654e+00 2.93548954e+00 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-1.29310345e+01 -2.02970297e+01 3.35403727e+01 2.04651163e+01 7.72200772e+00 1.43369176e+00 -7.77385159e+00 -1.80076628e+01 9.34579439e+00 2.13675214e+00 -2.59696459e+00 -5.54016620e-01 8.23467967e+00 4.18208139e-01 -3.60403652e+00 5.38384845e+00 -4.58845790e+00 6.80879194e+00 -2.16927124e+01 2.67338471e+01 6.04926723e+00 -5.71040109e+00 1.26171593e+01 2.76568502e+01 -8.32497492e+00 1.33479212e+01 1.20656371e+01 -1.42118863e+00 -9.48012232e+00 -5.98455598e+00 3.31108830e+01 7.98303124e+00 9.82175336e-01 5.04322767e-01 1.73835125e+01 5.52671756e+00 1.11689815e+01 1.44195731e+01 1.00090992e+00 1.13738739e+01 -1.26188069e+01 5.27655635e+00 -3.20949659e+00 2.38439306e+00] Predicted: [-1.05884926 -0.09661985 0.86560957 1.82783899 2.79006841 3.75229782 4.71452724 5.67675666 6.63898607 7.60121549 8.56344491 -1.05884926 -0.09661985 0.86560957 1.82783899 2.79006841 3.75229782 4.71452724 5.67675666 6.63898607 7.60121549 8.56344491 -1.05884926 -0.09661985 0.86560957 1.82783899 2.79006841 3.75229782 4.71452724 5.67675666 6.63898607 7.60121549 8.56344491 -1.05884926 -0.09661985 0.86560957 1.82783899 2.79006841 3.75229782 4.71452724 5.67675666 6.63898607 7.60121549 8.56344491 -1.05884926 -0.09661985 0.86560957 1.82783899 2.79006841 3.75229782 4.71452724 5.67675666 6.63898607 7.60121549 8.56344491 -1.05884926 -0.09661985 0.86560957 1.82783899 2.79006841 3.75229782 4.71452724 5.67675666 6.63898607 7.60121549 8.56344491 -1.05884926 -0.09661985 0.86560957 1.82783899 2.79006841 3.75229782 4.71452724 5.67675666 6.63898607 7.60121549 8.56344491 -1.05884926 -0.09661985 0.86560957 1.82783899 2.79006841 3.75229782 4.71452724 5.67675666 6.63898607 7.60121549 8.56344491 -1.05884926 -0.09661985 0.86560957 1.82783899 2.79006841 3.75229782 4.71452724 5.67675666 6.63898607 7.60121549 8.56344491 -1.05884926 -0.09661985 0.86560957 1.82783899 2.79006841 3.75229782 4.71452724 5.67675666 6.63898607 7.60121549 8.56344491 -1.05884926 -0.09661985 0.86560957 1.82783899 2.79006841 3.75229782 4.71452724 5.67675666 6.63898607 7.60121549 8.56344491 -1.05884926 -0.09661985 0.86560957 1.82783899 2.79006841 3.75229782 4.71452724 5.67675666 6.63898607 7.60121549 8.56344491 -1.05884926 -0.09661985 0.86560957 1.82783899 2.79006841 3.75229782 4.71452724 5.67675666 6.63898607 7.60121549 8.56344491 -1.05884926 -0.09661985 0.86560957 1.82783899 2.79006841 3.75229782 4.71452724 5.67675666 6.63898607 7.60121549 8.56344491 -1.05884926 -0.09661985 0.86560957 1.82783899 2.79006841 3.75229782 4.71452724 5.67675666 6.63898607 7.60121549 8.56344491 -1.05884926 -0.09661985 0.86560957 1.82783899 2.79006841 3.75229782 4.71452724 5.67675666 6.63898607 7.60121549 8.56344491 -1.05884926 -0.09661985 0.86560957 1.82783899 2.79006841 3.75229782 4.71452724 5.67675666 6.63898607 7.60121549 8.56344491 -1.05884926 -0.09661985 0.86560957 1.82783899 2.79006841 3.75229782 4.71452724 5.67675666 6.63898607 7.60121549 8.56344491 -1.05884926 -0.09661985 0.86560957 1.82783899 2.79006841 3.75229782 4.71452724 5.67675666 6.63898607 7.60121549 8.56344491 -1.05884926 -0.09661985 0.86560957 1.82783899 2.79006841 3.75229782 4.71452724 5.67675666 6.63898607 7.60121549 8.56344491 -1.05884926 -0.09661985 0.86560957 1.82783899 2.79006841 3.75229782 4.71452724 5.67675666 6.63898607 7.60121549 8.56344491 -1.05884926 -0.09661985 0.86560957 1.82783899 2.79006841 3.75229782 4.71452724 5.67675666 6.63898607 7.60121549 8.56344491 -1.05884926 -0.09661985 0.86560957 1.82783899 2.79006841 3.75229782 4.71452724 5.67675666 6.63898607 7.60121549 8.56344491 -1.05884926 -0.09661985 0.86560957 1.82783899 2.79006841 3.75229782 4.71452724 5.67675666 6.63898607 7.60121549 8.56344491 -1.05884926 -0.09661985 0.86560957 1.82783899 2.79006841 3.75229782 4.71452724 5.67675666 6.63898607 7.60121549 8.56344491 -1.05884926 -0.09661985 0.86560957 1.82783899 2.79006841 3.75229782 4.71452724 5.67675666 6.63898607 7.60121549 8.56344491 -1.05884926 -0.09661985 0.86560957 1.82783899 2.79006841 3.75229782 4.71452724 5.67675666 6.63898607 7.60121549 8.56344491 -1.05884926 -0.09661985 0.86560957 1.82783899 2.79006841 3.75229782 4.71452724 5.67675666 6.63898607 7.60121549 8.56344491 -1.05884926 -0.09661985 0.86560957 1.82783899 2.79006841 3.75229782 4.71452724 5.67675666 6.63898607 7.60121549 8.56344491 3.75229782 4.71452724 5.67675666 6.63898607 8.56344491 -1.05884926 -0.09661985 0.86560957 1.82783899 2.79006841 3.75229782 4.71452724 5.67675666 6.63898607 7.60121549 8.56344491 -1.05884926 -0.09661985 0.86560957 1.82783899 2.79006841 3.75229782 4.71452724 5.67675666 6.63898607 7.60121549 8.56344491 -1.05884926 -0.09661985 0.86560957 1.82783899 2.79006841 3.75229782 4.71452724 5.67675666 6.63898607 7.60121549 8.56344491 -1.05884926 -0.09661985 0.86560957 1.82783899 2.79006841 3.75229782 4.71452724 5.67675666 6.63898607 7.60121549 8.56344491 -1.05884926 -0.09661985 0.86560957 1.82783899 2.79006841 3.75229782 4.71452724 5.67675666 6.63898607 7.60121549 8.56344491 -1.05884926 -0.09661985 0.86560957 1.82783899 2.79006841 3.75229782 4.71452724 5.67675666 6.63898607 7.60121549 8.56344491 -1.05884926 -0.09661985 0.86560957 1.82783899 2.79006841 3.75229782 4.71452724 5.67675666 6.63898607 7.60121549 8.56344491 -1.05884926 -0.09661985 0.86560957 1.82783899 2.79006841 3.75229782 4.71452724 5.67675666 6.63898607 7.60121549 8.56344491 -1.05884926 -0.09661985 0.86560957 1.82783899 2.79006841 3.75229782 4.71452724 5.67675666 6.63898607 7.60121549 8.56344491 -1.05884926 -0.09661985 0.86560957 1.82783899 2.79006841 3.75229782 4.71452724 5.67675666 6.63898607 7.60121549 8.56344491 -1.05884926 -0.09661985 0.86560957 1.82783899 2.79006841 3.75229782 4.71452724 5.67675666 6.63898607 7.60121549 8.56344491 -1.05884926 -0.09661985 0.86560957 1.82783899 2.79006841 3.75229782 4.71452724 5.67675666 6.63898607 7.60121549 8.56344491 -1.05884926 -0.09661985 0.86560957 1.82783899 2.79006841 3.75229782 4.71452724 5.67675666 6.63898607 7.60121549 8.56344491 -1.05884926 -0.09661985 0.86560957 1.82783899 2.79006841 3.75229782 4.71452724 5.67675666 6.63898607 7.60121549 8.56344491 -1.05884926 -0.09661985 0.86560957 1.82783899 2.79006841 3.75229782 4.71452724 5.67675666 6.63898607 7.60121549 8.56344491 -1.05884926 -0.09661985 0.86560957 1.82783899 2.79006841 3.75229782 4.71452724 5.67675666 6.63898607 7.60121549 8.56344491 -1.05884926 -0.09661985 0.86560957 1.82783899 2.79006841 3.75229782 4.71452724 5.67675666 6.63898607 7.60121549 8.56344491 -1.05884926 -0.09661985 0.86560957 1.82783899 2.79006841 3.75229782 4.71452724 5.67675666 6.63898607 7.60121549 8.56344491 -1.05884926 -0.09661985 0.86560957 1.82783899 2.79006841 3.75229782 4.71452724 5.67675666 6.63898607 7.60121549 8.56344491 -1.05884926 -0.09661985 0.86560957 1.82783899 2.79006841 3.75229782 4.71452724 5.67675666 6.63898607 7.60121549 8.56344491 -1.05884926 -0.09661985 0.86560957 1.82783899 2.79006841 3.75229782 4.71452724 5.67675666 6.63898607 7.60121549 8.56344491 -1.05884926] Girls Growth: Actual: [-1.03448276e+01 -8.97435897e+00 1.26760563e+01 1.25000000e+00 3.20987654e+01 7.47663551e+00 -6.95652174e+00 4.67289720e+00 -2.58928571e+01 3.61445783e+01 9.73451327e+00 -5.18617021e+00 8.34502104e+00 -7.11974110e-01 8.08344198e+00 -2.11097708e+00 7.88662970e+00 3.82638492e+00 -2.75027503e-01 -1.90843905e+01 2.61077028e+01 3.13513514e+00 1.25628141e+00 8.85028950e+00 9.57446809e+00 1.24826630e+00 1.23287671e+01 6.76829268e+00 3.65505425e+00 8.26446281e+00 -2.05089059e+01 3.58514725e+01 5.84354383e+00 1.96618168e+00 6.36328577e+00 2.97316896e+00 5.80985915e+00 3.06156406e+00 8.87956087e+00 1.12692764e+00 -3.46041056e+00 -2.14763062e+01 2.08897485e+01 3.16800000e+00 4.01606426e+00 9.26640927e+00 7.42049470e+00 9.86842105e-01 8.14332248e+00 6.92771084e+00 4.22535211e+00 8.10810811e-01 -1.60857909e+00 2.80653951e+01 -2.76595745e+00 -2.31481481e+01 2.04819277e+01 -2.00000000e+00 1.02040816e+00 -3.03030303e+00 4.16666667e+00 3.90000000e+01 2.15827338e+00 -1.61971831e+01 2.18487395e+01 1.72413793e+01 -2.89855072e+00 4.90405117e+00 1.38211382e+01 2.00000000e+01 1.30952381e+01 1.44736842e+00 4.28015564e+00 2.20149254e+01 -6.01427115e+00 3.03687636e+01 1.10648918e+01 1.60000000e+00 7.87401575e+00 3.21167883e+01 5.52486188e-01 2.14285714e+01 4.52488688e+00 -1.08225108e+01 4.85436893e+00 -6.48148148e+00 2.37623762e+01 4.40000000e+00 -4.89260143e+00 -2.82308657e+00 -1.61394448e-01 5.75493049e+00 8.68236013e+00 8.94514768e+00 6.40330493e+00 5.82382917e+00 -1.39646870e+01 2.97974414e+01 1.43737166e+00 -1.06649937e+00 3.74128091e+00 5.03259984e+00 2.10475267e+00 1.57689750e+00 1.51500982e+00 4.71672041e+00 -1.87384534e+00 -1.37170522e+01 1.89941812e+01 -6.98567936e-02 6.06060606e-01 -7.53012048e-01 3.92369391e+00 3.96328744e+00 2.42776886e+00 -1.25367287e+00 4.46340012e+00 2.60159514e+00 -2.36165093e+01 2.76714320e+01 1.70810400e+00 1.03542234e+01 5.67901235e+00 1.58878505e+01 3.83064516e+00 -3.88349515e+00 2.42424242e+00 6.31163708e+00 2.04081633e+00 -4.54545455e+00 2.03809524e+01 2.21518987e+00 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1.37254902e+01 2.32758621e+01 8.28671329e+01 -1.39579350e+01 4.70588235e+01 3.20000000e+01 1.31313131e+01 1.83035714e+01 1.47169811e+01 2.30263158e+00 8.68167203e+00 2.95857988e-01 -3.83480826e+01 5.45454545e+01 1.36222910e+01 4.17690418e+00 4.71698113e-01 1.00938967e+01 1.64179104e+01 8.05860806e+00 1.08474576e+01 4.35779817e+00 7.17948718e+00 -3.55434040e+01 8.65323436e+01 1.00625355e+01 -4.14012739e+00 1.32890365e+00 6.65573770e+01 5.31496063e+00 8.41121495e+00 3.05172414e+01 9.24702774e-01 1.96335079e+00 -1.86136072e+01 4.08517350e+01 1.87010078e+01 -3.19819820e+00 2.04746394e+00 0.00000000e+00 3.73917009e+00 6.85714286e+00 1.06129165e+01 3.68166605e+00 1.29124821e+00 -2.52832861e+01 4.25592417e+01 9.40824468e+00 3.43642612e-01 5.99315068e+00 1.24394184e+01 -3.16091954e+00 6.97329377e+00 -1.10957004e+00 9.11640954e+00 9.76863753e+00 -2.42388759e+01 3.57032457e+01 7.40318907e+00 -5.26315789e-01 0.00000000e+00 8.46560847e+00 1.12195122e+01 -7.01754386e+00 5.66037736e+00 -1.78571429e+00 9.09090909e+00 -3.75000000e+01 6.46666667e+01 2.10526316e+01 8.33333333e+00 6.08695652e+01 1.08108108e+01 5.85365854e+01 3.07692308e+00 2.23880597e+01 -9.75609756e+00 -3.24324324e+01 4.80000000e+01 2.83783784e+01 3.89048991e+00 6.93481276e+00 1.29701686e-01 4.66321244e+00 9.15841584e+00 8.04988662e+00 4.40713536e+00 3.01507538e+00 -3.80487805e+00 3.72210953e+01 -8.13008130e-01 -1.19904077e+00 -5.09708738e+00 9.20716113e+00 1.77985948e+01 5.96421471e+00 -1.31332083e+00 4.37262357e+00 -6.92167577e+00 -1.52641879e+01 2.97921478e+01 3.20284698e+00 -2.17821782e+01 7.59493671e+00 3.88235294e+01 6.77966102e+00 3.57142857e+01 2.45614035e+01 1.03286385e+01 -4.68085106e+00 -2.36607143e+01 2.16374269e+01 4.32692308e+00 5.55555556e+00 0.00000000e+00 4.73684211e+01 2.70270270e+00 -1.05263158e+01 5.00000000e+01 -1.96078431e+00 6.66666667e+00 1.00000000e+01 1.70454545e+00 9.49720670e+00 1.07142857e+01 2.11981567e+01 9.12547529e+00 -6.27177700e+00 -3.42007435e+01 5.53672316e+01 -2.90909091e+00 -7.96178344e-01 2.72873194e+00 5.62500000e+00 7.54437870e+00 1.85694635e+01 4.75638051e+00 1.42857143e+01 2.18023256e+01 -1.80588703e+01 3.59223301e+01 8.35714286e+00 -1.46341463e+01 -7.85714286e+00 1.78294574e+01 -6.57894737e-01 -5.96026490e+00 2.18309859e+01 1.73410405e+00 5.68181818e-01 -1.75141243e+01 3.28767123e+01 8.24742268e+00 3.07692308e+01 2.35294118e+01 3.17460317e+00 -4.00000000e+01 -2.30769231e+01 2.35798499e+00 7.12041885e+00 1.25122190e+01 4.95221546e+00 6.29139073e+00 9.81308411e+00 4.39716312e+00 -1.08695652e+00 -2.18406593e+01 2.72407733e+01 5.04143646e+00 -2.80000000e+01 -2.59259259e+01 1.50000000e+01 0.00000000e+00 -4.61538462e+01 -1.42857143e+01 -7.28051392e+00 6.92840647e-01 5.50458716e+00 5.86956522e+00 5.33880903e+00 2.47563353e+01 1.46875000e+01 8.58310627e+00 -2.91091593e+01 3.75221239e+01 9.65250965e+00 1.37931034e+01 -1.36363636e+01 7.01754386e+01 -1.13402062e+01 2.20930233e+01 1.80952381e+01 1.12903226e+01 -1.44927536e+00 -5.88235294e+00 5.46875000e+01 5.05050505e+00 8.39694656e+00 4.22535211e+00 1.75675676e+01 1.83908046e+01 3.25242718e+01 2.60073260e+01 -5.23255814e+00 5.21472393e+00 -1.22448980e+01 2.72425249e+01 1.22715405e+01 3.04487179e+00 9.48678072e+00 9.37500000e+00 9.61038961e+00 8.05687204e+00 7.45614035e+00 -9.18367347e-01 9.37178167e+00 -2.33521657e+01 3.30466830e+01 1.08033241e+01 -1.48743477e+00 2.32876712e+00 3.73940205e+00 4.77747190e+00 3.77141606e+00 4.64579597e+00 4.35133262e+00 1.56265095e+00 -1.62944995e+01 2.49623569e+01 3.73191165e+00] Predicted: [3.4014558 3.90830557 4.41515535 4.92200512 5.4288549 5.93570468 6.44255445 6.94940423 7.456254 7.96310378 8.46995355 3.4014558 3.90830557 4.41515535 4.92200512 5.4288549 5.93570468 6.44255445 6.94940423 7.456254 7.96310378 8.46995355 3.4014558 3.90830557 4.41515535 4.92200512 5.4288549 5.93570468 6.44255445 6.94940423 7.456254 7.96310378 8.46995355 3.4014558 3.90830557 4.41515535 4.92200512 5.4288549 5.93570468 6.44255445 6.94940423 7.456254 7.96310378 8.46995355 3.4014558 3.90830557 4.41515535 4.92200512 5.4288549 5.93570468 6.44255445 6.94940423 7.456254 7.96310378 8.46995355 3.4014558 3.90830557 4.41515535 4.92200512 5.4288549 5.93570468 6.44255445 6.94940423 7.456254 7.96310378 8.46995355 3.4014558 3.90830557 4.41515535 4.92200512 5.4288549 5.93570468 6.44255445 6.94940423 7.456254 7.96310378 8.46995355 3.4014558 3.90830557 4.41515535 4.92200512 5.4288549 5.93570468 6.44255445 6.94940423 7.456254 7.96310378 8.46995355 3.4014558 3.90830557 4.41515535 4.92200512 5.4288549 5.93570468 6.44255445 6.94940423 7.456254 7.96310378 8.46995355 3.4014558 3.90830557 4.41515535 4.92200512 5.4288549 5.93570468 6.44255445 6.94940423 7.456254 7.96310378 8.46995355 3.4014558 3.90830557 4.41515535 4.92200512 5.4288549 5.93570468 6.44255445 6.94940423 7.456254 7.96310378 8.46995355 3.4014558 3.90830557 4.41515535 4.92200512 5.4288549 5.93570468 6.44255445 6.94940423 7.456254 7.96310378 8.46995355 3.4014558 3.90830557 4.41515535 4.92200512 5.4288549 5.93570468 6.44255445 6.94940423 7.456254 7.96310378 8.46995355 3.4014558 3.90830557 4.41515535 4.92200512 5.4288549 5.93570468 6.44255445 6.94940423 7.456254 7.96310378 8.46995355 3.4014558 3.90830557 4.41515535 4.92200512 5.4288549 5.93570468 6.44255445 6.94940423 7.456254 7.96310378 8.46995355 3.4014558 3.90830557 4.41515535 4.92200512 5.4288549 5.93570468 6.44255445 6.94940423 7.456254 7.96310378 8.46995355 3.4014558 3.90830557 4.41515535 4.92200512 5.4288549 5.93570468 6.44255445 6.94940423 7.456254 7.96310378 8.46995355 3.4014558 3.90830557 4.41515535 4.92200512 5.4288549 5.93570468 6.44255445 6.94940423 7.456254 7.96310378 8.46995355 3.4014558 3.90830557 4.41515535 4.92200512 5.4288549 5.93570468 6.44255445 6.94940423 7.456254 7.96310378 8.46995355 3.4014558 3.90830557 4.41515535 4.92200512 5.4288549 5.93570468 6.44255445 6.94940423 7.456254 7.96310378 8.46995355 3.4014558 3.90830557 4.41515535 4.92200512 5.4288549 5.93570468 6.44255445 6.94940423 7.456254 7.96310378 8.46995355 3.4014558 3.90830557 4.41515535 4.92200512 5.4288549 5.93570468 6.44255445 6.94940423 7.456254 7.96310378 8.46995355 3.4014558 3.90830557 4.41515535 4.92200512 5.4288549 5.93570468 6.44255445 6.94940423 7.456254 7.96310378 8.46995355 3.4014558 3.90830557 4.41515535 4.92200512 5.4288549 5.93570468 6.44255445 6.94940423 7.456254 7.96310378 8.46995355 3.4014558 3.90830557 4.41515535 4.92200512 5.4288549 5.93570468 6.44255445 6.94940423 7.456254 7.96310378 8.46995355 3.4014558 3.90830557 4.41515535 4.92200512 5.4288549 5.93570468 6.44255445 6.94940423 7.456254 7.96310378 8.46995355 3.4014558 3.90830557 4.41515535 4.92200512 5.4288549 5.93570468 6.44255445 6.94940423 7.456254 7.96310378 8.46995355 3.4014558 3.90830557 4.41515535 4.92200512 5.4288549 5.93570468 6.44255445 6.94940423 7.456254 7.96310378 8.46995355 3.4014558 3.90830557 4.41515535 4.92200512 5.4288549 5.93570468 6.44255445 6.94940423 7.456254 7.96310378 8.46995355 5.93570468 6.44255445 6.94940423 7.456254 8.46995355 3.4014558 3.90830557 4.41515535 4.92200512 5.4288549 5.93570468 6.44255445 6.94940423 7.456254 7.96310378 8.46995355 3.4014558 3.90830557 4.41515535 4.92200512 5.4288549 5.93570468 6.44255445 6.94940423 7.456254 7.96310378 8.46995355 3.4014558 3.90830557 4.41515535 4.92200512 5.4288549 5.93570468 6.44255445 6.94940423 7.456254 7.96310378 8.46995355 3.4014558 3.90830557 4.41515535 4.92200512 5.4288549 5.93570468 6.44255445 6.94940423 7.456254 7.96310378 8.46995355 3.4014558 3.90830557 4.41515535 4.92200512 5.4288549 5.93570468 6.44255445 6.94940423 7.456254 7.96310378 8.46995355 3.4014558 3.90830557 4.41515535 4.92200512 5.4288549 5.93570468 6.44255445 6.94940423 7.456254 7.96310378 8.46995355 3.4014558 3.90830557 4.41515535 4.92200512 5.4288549 5.93570468 6.44255445 6.94940423 7.456254 7.96310378 8.46995355 3.4014558 3.90830557 4.41515535 4.92200512 5.4288549 5.93570468 6.44255445 6.94940423 7.456254 7.96310378 8.46995355 3.4014558 3.90830557 4.41515535 4.92200512 5.4288549 5.93570468 6.44255445 6.94940423 7.456254 7.96310378 8.46995355 3.4014558 3.90830557 4.41515535 4.92200512 5.4288549 5.93570468 6.44255445 6.94940423 7.456254 7.96310378 8.46995355 3.4014558 3.90830557 4.41515535 4.92200512 5.4288549 5.93570468 6.44255445 6.94940423 7.456254 7.96310378 8.46995355 3.4014558 3.90830557 4.41515535 4.92200512 5.4288549 5.93570468 6.44255445 6.94940423 7.456254 7.96310378 8.46995355 3.4014558 3.90830557 4.41515535 4.92200512 5.4288549 5.93570468 6.44255445 6.94940423 7.456254 7.96310378 8.46995355 3.4014558 3.90830557 4.41515535 4.92200512 5.4288549 5.93570468 6.44255445 6.94940423 7.456254 7.96310378 8.46995355 3.4014558 3.90830557 4.41515535 4.92200512 5.4288549 5.93570468 6.44255445 6.94940423 7.456254 7.96310378 8.46995355 3.4014558 3.90830557 4.41515535 4.92200512 5.4288549 5.93570468 6.44255445 6.94940423 7.456254 7.96310378 8.46995355 3.4014558 3.90830557 4.41515535 4.92200512 5.4288549 5.93570468 6.44255445 6.94940423 7.456254 7.96310378 8.46995355 3.4014558 3.90830557 4.41515535 4.92200512 5.4288549 5.93570468 6.44255445 6.94940423 7.456254 7.96310378 8.46995355 3.4014558 3.90830557 4.41515535 4.92200512 5.4288549 5.93570468 6.44255445 6.94940423 7.456254 7.96310378 8.46995355 3.4014558 3.90830557 4.41515535 4.92200512 5.4288549 5.93570468 6.44255445 6.94940423 7.456254 7.96310378 8.46995355 3.4014558 3.90830557 4.41515535 4.92200512 5.4288549 5.93570468 6.44255445 6.94940423 7.456254 7.96310378 8.46995355 3.4014558 ] Age Group: 19&over Boys Growth: Actual: [-6.16883117e+00 -5.53633218e+00 4.02930403e+00 1.76056338e+00 -5.88235294e+00 2.57352941e+00 7.88530466e+00 -8.63787375e+00 -3.89090909e+01 7.08333333e+01 -1.35888502e+01 2.85667378e+00 -1.30885874e+00 8.08668931e-02 -3.16742081e+00 1.90253672e+00 -6.14150016e+00 2.44285465e+00 -7.15380685e+00 -2.22711429e+01 3.32074581e+01 -4.78384125e+00 1.48535565e+01 -1.78101599e+00 -4.12116217e-02 8.53432282e+00 -1.04463438e+00 -6.67946257e+00 1.09214315e+01 -3.74559614e+00 -4.08399152e+01 5.03419082e+01 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8.06993948e-01 8.93929286e+00 5.93998775e+00 2.19653179e+00 3.24095023e+01 -1.31567706e+01 2.70536153e+00 -5.36398467e+00 5.30016225e+00] Predicted: [ 0.55502035 1.76399288 2.97296541 4.18193795 5.39091048 6.59988301 7.80885555 9.01782808 10.22680061 11.43577314 12.64474568 0.55502035 1.76399288 2.97296541 4.18193795 5.39091048 6.59988301 7.80885555 9.01782808 10.22680061 11.43577314 12.64474568 0.55502035 1.76399288 2.97296541 4.18193795 5.39091048 6.59988301 7.80885555 9.01782808 10.22680061 11.43577314 12.64474568 0.55502035 1.76399288 2.97296541 4.18193795 5.39091048 6.59988301 7.80885555 9.01782808 10.22680061 11.43577314 12.64474568 0.55502035 1.76399288 2.97296541 4.18193795 5.39091048 6.59988301 7.80885555 9.01782808 10.22680061 11.43577314 12.64474568 0.55502035 1.76399288 2.97296541 4.18193795 5.39091048 6.59988301 7.80885555 9.01782808 10.22680061 11.43577314 12.64474568 0.55502035 1.76399288 2.97296541 4.18193795 5.39091048 6.59988301 7.80885555 9.01782808 10.22680061 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11.43577314 12.64474568 0.55502035 1.76399288 2.97296541 4.18193795 5.39091048 6.59988301 7.80885555 9.01782808 10.22680061 11.43577314 12.64474568 0.55502035 1.76399288 2.97296541 4.18193795 5.39091048 6.59988301 7.80885555 9.01782808 10.22680061 11.43577314 12.64474568 0.55502035 1.76399288 2.97296541 4.18193795 5.39091048 6.59988301 7.80885555 9.01782808 10.22680061 11.43577314 12.64474568 0.55502035 1.76399288 2.97296541 4.18193795 5.39091048 6.59988301 7.80885555 9.01782808 10.22680061 11.43577314 12.64474568 0.55502035 1.76399288 2.97296541 4.18193795 5.39091048 6.59988301 7.80885555 9.01782808 10.22680061 11.43577314 12.64474568 0.55502035 1.76399288 2.97296541 4.18193795 5.39091048 6.59988301 7.80885555 9.01782808 10.22680061 11.43577314 12.64474568 6.59988301 7.80885555 9.01782808 10.22680061 12.64474568 0.55502035 1.76399288 2.97296541 4.18193795 5.39091048 6.59988301 7.80885555 9.01782808 10.22680061 11.43577314 12.64474568 0.55502035 1.76399288 2.97296541 4.18193795 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5.39091048 6.59988301 7.80885555 9.01782808 10.22680061 11.43577314 12.64474568 0.55502035 1.76399288 2.97296541 4.18193795 5.39091048 6.59988301 7.80885555 9.01782808 10.22680061 11.43577314 12.64474568 0.55502035 1.76399288 2.97296541 4.18193795 5.39091048 6.59988301 7.80885555 9.01782808 10.22680061 11.43577314 12.64474568 0.55502035 1.76399288 2.97296541 4.18193795 5.39091048 6.59988301 7.80885555 9.01782808 10.22680061 11.43577314 12.64474568 0.55502035 1.76399288 2.97296541 4.18193795 5.39091048 6.59988301 7.80885555 9.01782808 10.22680061 11.43577314 12.64474568 0.55502035 1.76399288 2.97296541 4.18193795 5.39091048 6.59988301 7.80885555 9.01782808 10.22680061 11.43577314 12.64474568 0.55502035 1.76399288 2.97296541 4.18193795 5.39091048 6.59988301 7.80885555 9.01782808 10.22680061 11.43577314 12.64474568 0.55502035 1.76399288 2.97296541 4.18193795 5.39091048 6.59988301 7.80885555 9.01782808 10.22680061 11.43577314 12.64474568 0.55502035 1.76399288 2.97296541 4.18193795 5.39091048 6.59988301 7.80885555 9.01782808 10.22680061 11.43577314 12.64474568 0.55502035 1.76399288 2.97296541 4.18193795 5.39091048 6.59988301 7.80885555 9.01782808 10.22680061 11.43577314 12.64474568 0.55502035 1.76399288 2.97296541 4.18193795 5.39091048 6.59988301 7.80885555 9.01782808 10.22680061 11.43577314 12.64474568 0.55502035 1.76399288 2.97296541 4.18193795 5.39091048 6.59988301 7.80885555 9.01782808 10.22680061 11.43577314 12.64474568 0.55502035] Girls Growth: Actual: [-1.80327869e+01 -1.80000000e+01 9.75609756e+00 -8.88888889e+00 -2.43902439e+00 -1.50000000e+01 5.88235294e+00 1.11111111e+01 -7.00000000e+01 1.50000000e+02 0.00000000e+00 -4.01396161e+00 -2.36363636e+00 2.23463687e+00 -3.46083789e+00 -3.39622642e+00 -1.56250000e+00 6.15079365e+00 1.68224299e+00 -4.98161765e+01 1.02197802e+02 1.26811594e+00 5.28455285e+00 2.23938224e+01 -4.41640379e+00 0.00000000e+00 4.62046205e+00 7.57097792e+00 -5.57184751e+00 -6.21118012e-01 -5.28125000e+01 1.24503311e+02 6.48967552e+00 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3.18545411 4.27062766 5.35580121 6.44097476 7.52614831 8.61132186 9.69649541 10.78166896 11.8668425 12.95201605 14.0371896 3.18545411 4.27062766 5.35580121 6.44097476 7.52614831 8.61132186 9.69649541 10.78166896 11.8668425 12.95201605 14.0371896 3.18545411 4.27062766 5.35580121 6.44097476 7.52614831 8.61132186 9.69649541 10.78166896 11.8668425 12.95201605 14.0371896 3.18545411] Age Group: 17-18 Boys Growth: Actual: [-2.69841270e+01 5.43478261e+00 1.03092784e+01 -2.52336449e+01 1.12500000e+01 6.74157303e+00 -1.89473684e+01 -3.89610390e+00 -2.70270270e+00 -4.16666667e+00 -1.15942029e+01 -5.37634409e-01 -5.94594595e+00 -2.15517241e-01 2.95176386e+00 -4.68531469e+00 -7.70359501e+00 8.74403816e-01 -6.14657210e+00 -6.21326616e+00 1.70098478e+00 5.10563380e+00 -2.86512928e+00 -3.52517986e+00 -3.05741984e+00 1.53846154e-01 3.22580645e+00 0.00000000e+00 6.32440476e+00 -8.11756473e+00 -9.44402133e+00 5.55088310e+00 -5.25896414e+00 1.20481928e+00 2.50626566e-01 1.25000000e+00 2.53086420e+00 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0.0962021 1.41877585 2.74134961 4.06392336 5.38649711 6.70907087 8.03164462 9.35421838 -3.87151916 -2.54894541 -1.22637166 0.0962021 1.41877585 2.74134961 4.06392336 5.38649711 6.70907087 8.03164462 9.35421838 -3.87151916 -2.54894541 -1.22637166 0.0962021 1.41877585 2.74134961 4.06392336 5.38649711 6.70907087 8.03164462 9.35421838 -3.87151916 -2.54894541 -1.22637166 0.0962021 1.41877585 2.74134961 5.38649711 6.70907087 8.03164462 9.35421838 -3.87151916 -2.54894541 0.0962021 1.41877585 2.74134961 4.06392336 5.38649711 6.70907087 8.03164462 9.35421838 -3.87151916 -2.54894541 -1.22637166 0.0962021 1.41877585 2.74134961 4.06392336 5.38649711 6.70907087 8.03164462 9.35421838 -3.87151916 -2.54894541 -1.22637166 0.0962021 1.41877585 2.74134961 4.06392336 5.38649711 6.70907087 8.03164462 9.35421838 -3.87151916 -2.54894541 -1.22637166 0.0962021 1.41877585 4.06392336 5.38649711 8.03164462 9.35421838 -3.87151916 -2.54894541 -1.22637166 0.0962021 1.41877585 2.74134961 4.06392336 5.38649711 6.70907087 8.03164462 9.35421838 -3.87151916 -2.54894541 -1.22637166 0.0962021 1.41877585 2.74134961 4.06392336 5.38649711 6.70907087 8.03164462 9.35421838 -3.87151916 -2.54894541 -1.22637166 0.0962021 1.41877585 2.74134961 4.06392336 5.38649711 6.70907087 8.03164462 9.35421838 -3.87151916 -2.54894541 0.0962021 1.41877585 2.74134961 4.06392336 5.38649711 6.70907087 8.03164462 9.35421838 -3.87151916 -2.54894541 -1.22637166 0.0962021 1.41877585 2.74134961 4.06392336 6.70907087 8.03164462 -3.87151916 -2.54894541 -1.22637166 0.0962021 1.41877585 2.74134961 4.06392336 5.38649711 6.70907087 8.03164462 9.35421838 -3.87151916 -2.54894541 -1.22637166 0.0962021 1.41877585 2.74134961 4.06392336 5.38649711 6.70907087 8.03164462 9.35421838 -3.87151916 -2.54894541 -1.22637166 0.0962021 1.41877585 2.74134961 4.06392336 5.38649711 6.70907087 8.03164462 9.35421838 -3.87151916 -2.54894541 -1.22637166 0.0962021 1.41877585 2.74134961 4.06392336 5.38649711 6.70907087 8.03164462 9.35421838 -3.87151916 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8.42105263e+01 1.14285714e+01 -2.56410256e+01 -1.72413793e+01 3.33333333e+01 9.37500000e+00 0.00000000e+00 3.75000000e+01 -9.09090909e+00 -2.00000000e+01 -2.50000000e+01 1.16666667e+02 -3.84615385e+01 3.75000000e+01 9.09090909e+00 -4.16666667e+01 8.57142857e+01 1.53846154e+01 1.00000000e+02 0.00000000e+00 5.00000000e+01 -6.66666667e+01 0.00000000e+00 -1.00000000e+02 1.00000000e+02 5.00000000e+01 -1.87500000e+01 -1.53846154e+01 6.81818182e+01 -2.70270270e+01 -1.48148148e+01 3.47826087e+01 0.00000000e+00 3.87096774e+01 -2.32558140e+00 2.14285714e+01 -7.84313725e+00 -2.50000000e+01 -6.66666667e+00 5.00000000e+01 6.66666667e+01 5.71428571e+00 -2.16216216e+01 -1.03448276e+01 3.46153846e+01 2.85714286e+01 -2.88888889e+01 -6.25000000e+00 3.33333333e+01 -7.50000000e+01 1.00000000e+02 1.00000000e+02 7.50000000e+01 -4.28571429e+01 2.50000000e+01 -6.00000000e+01 1.00000000e+02 5.00000000e+01 -5.00000000e+01 -6.66666667e+01 0.00000000e+00 0.00000000e+00 0.00000000e+00 -1.00000000e+02 2.00000000e+01 0.00000000e+00 0.00000000e+00 -1.66666667e+01 4.00000000e+01 -4.28571429e+01 5.00000000e+01 1.66666667e+01 2.85714286e+01 3.33333333e+01 -2.50000000e+01 2.72727273e+01 -7.14285714e+00 -2.30769231e+01 1.00000000e+01 5.45454545e+01 -1.47058824e+01 2.41379310e+01 1.94444444e+01 -2.32558140e+00 4.28571429e+01 5.00000000e+00 -4.00000000e+01 0.00000000e+00 0.00000000e+00 0.00000000e+00 -1.66666667e+01 6.00000000e+01 7.50000000e+01 -2.85714286e+01 3.00000000e+01 0.00000000e+00 0.00000000e+00 -1.00000000e+02 1.00000000e+02 5.00000000e+01 -6.66666667e+01 -1.17647059e+01 -1.50000000e+01 1.56862745e+01 4.91525424e+01 -2.61363636e+01 2.76923077e+01 1.44578313e+01 3.26315789e+01 -2.38095238e+01 4.16666667e+00 2.30000000e+01 -1.00000000e+02 -5.00000000e+01 -1.00000000e+02 -1.00000000e+02 -5.00000000e+00 -2.10526316e+01 -1.33333333e+01 3.07692308e+01 1.17647059e+01 -2.10526316e+01 1.00000000e+02 3.33333333e+00 -9.67741935e+00 1.78571429e+01 9.09090909e+00 6.66666667e+01 -2.00000000e+01 -2.50000000e+01 3.33333333e+01 -2.50000000e+01 6.66666667e+01 -8.00000000e+01 0.00000000e+00 4.00000000e+02 1.00000000e+02 2.00000000e+01 -2.00000000e+01 2.50000000e+01 1.00000000e+02 1.00000000e+01 0.00000000e+00 -9.09090909e+00 2.00000000e+01 -4.16666667e+01 4.28571429e+01 1.10000000e+02 4.28571429e+01 -4.65116279e+01 3.47826087e+01 4.83870968e+01 2.17391304e+00 2.76595745e+01 -1.16666667e+01 -2.07547170e+01 3.57142857e+01 -3.50877193e+00 -3.63636364e+00 5.66037736e+00 -1.39318885e+00 -3.83045526e+00 4.99510284e+00 3.82462687e+00 2.27613058e+00 2.89897511e+00 2.41889585e+00 4.16782440e+00 4.50786877e+00 7.63144461e+00 2.91676547e+00] Predicted: [ 4.79438562 5.96656841 7.13875121 8.310934 9.4831168 10.65529959 11.82748238 12.99966518 14.17184797 15.34403077 16.51621356 4.79438562 5.96656841 7.13875121 8.310934 9.4831168 10.65529959 11.82748238 12.99966518 14.17184797 15.34403077 16.51621356 4.79438562 5.96656841 7.13875121 8.310934 9.4831168 10.65529959 11.82748238 12.99966518 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6.86498856e-01 -4.77272727e+00 1.95704057e+01 7.98403194e-01 4.31034483e+00 -1.81818182e+01 -5.05050505e+00 1.59574468e+01 7.33944954e+00 -2.56410256e+00 -4.38596491e+00 1.46788991e+01 -1.36000000e+01 4.62962963e+00 2.65486726e+00 -2.94117647e+00 3.03030303e+00 1.17647059e+01 -5.26315789e+00 -1.04166667e+00 1.19298246e+01 -5.95611285e+00 5.00000000e+00 4.44444444e+00 -5.47112462e+00 8.03858521e+00 -6.09756098e+00] Predicted: [-0.64332726 -0.05372458 0.53587809 1.12548077 1.71508344 2.30468612 2.8942888 3.48389147 4.07349415 4.66309682 5.2526995 -0.64332726 -0.05372458 0.53587809 1.12548077 1.71508344 2.30468612 2.8942888 3.48389147 4.07349415 4.66309682 5.2526995 -0.64332726 -0.05372458 0.53587809 1.12548077 1.71508344 2.30468612 2.8942888 3.48389147 4.07349415 4.66309682 5.2526995 -0.64332726 -0.05372458 0.53587809 1.12548077 1.71508344 2.30468612 2.8942888 3.48389147 4.07349415 4.66309682 5.2526995 -0.64332726 -0.05372458 0.53587809 1.12548077 1.71508344 2.30468612 2.8942888 3.48389147 4.07349415 4.66309682 5.2526995 -0.64332726 -0.05372458 0.53587809 1.12548077 1.71508344 2.30468612 2.8942888 3.48389147 4.07349415 4.66309682 5.2526995 -0.64332726 -0.05372458 0.53587809 1.12548077 1.71508344 2.30468612 2.8942888 3.48389147 4.07349415 4.66309682 5.2526995 -0.64332726 -0.05372458 0.53587809 1.12548077 1.71508344 2.30468612 2.8942888 3.48389147 4.07349415 4.66309682 5.2526995 -0.64332726 -0.05372458 0.53587809 1.12548077 1.71508344 2.30468612 2.8942888 3.48389147 4.07349415 4.66309682 5.2526995 -0.64332726 -0.05372458 0.53587809 1.12548077 1.71508344 2.30468612 2.8942888 3.48389147 4.07349415 4.66309682 5.2526995 -0.64332726 -0.05372458 0.53587809 1.12548077 1.71508344 2.30468612 2.8942888 3.48389147 4.07349415 4.66309682 5.2526995 -0.64332726 -0.05372458 0.53587809 1.12548077 1.71508344 2.30468612 2.8942888 3.48389147 4.07349415 4.66309682 5.2526995 -0.64332726 -0.05372458 0.53587809 1.12548077 1.71508344 2.30468612 2.8942888 3.48389147 4.07349415 4.66309682 5.2526995 -0.64332726 -0.05372458 0.53587809 1.12548077 1.71508344 2.30468612 2.8942888 3.48389147 4.07349415 4.66309682 5.2526995 -0.64332726 -0.05372458 0.53587809 1.12548077 1.71508344 2.30468612 2.8942888 3.48389147 4.07349415 4.66309682 5.2526995 -0.64332726 -0.05372458 0.53587809 1.12548077 1.71508344 2.30468612 2.8942888 3.48389147 4.07349415 4.66309682 5.2526995 -0.64332726 -0.05372458 0.53587809 1.12548077 1.71508344 2.30468612 2.8942888 3.48389147 4.07349415 4.66309682 5.2526995 -0.64332726 -0.05372458 0.53587809 1.12548077 1.71508344 2.30468612 2.8942888 3.48389147 4.07349415 4.66309682 5.2526995 -0.64332726 -0.05372458 0.53587809 1.12548077 1.71508344 2.30468612 2.8942888 3.48389147 4.07349415 4.66309682 5.2526995 -0.64332726 -0.05372458 0.53587809 1.12548077 1.71508344 2.30468612 2.8942888 3.48389147 4.07349415 4.66309682 5.2526995 -0.64332726 -0.05372458 0.53587809 1.12548077 1.71508344 2.30468612 2.8942888 3.48389147 4.07349415 4.66309682 5.2526995 -0.64332726 -0.05372458 0.53587809 1.12548077 1.71508344 2.30468612 2.8942888 3.48389147 4.07349415 4.66309682 5.2526995 -0.64332726 -0.05372458 0.53587809 1.12548077 1.71508344 2.30468612 2.8942888 3.48389147 4.07349415 4.66309682 5.2526995 -0.64332726 -0.05372458 0.53587809 1.12548077 1.71508344 2.30468612 2.8942888 3.48389147 4.07349415 4.66309682 5.2526995 -0.64332726 -0.05372458 0.53587809 1.12548077 1.71508344 2.30468612 2.8942888 3.48389147 4.07349415 4.66309682 5.2526995 -0.64332726 -0.05372458 0.53587809 1.12548077 1.71508344 2.30468612 2.8942888 3.48389147 4.07349415 4.66309682 5.2526995 -0.64332726 -0.05372458 0.53587809 1.12548077 1.71508344 2.30468612 2.8942888 3.48389147 4.07349415 4.66309682 5.2526995 -0.64332726 -0.05372458 0.53587809 1.12548077 1.71508344 2.30468612 2.8942888 3.48389147 4.07349415 4.66309682 5.2526995 -0.64332726 -0.05372458 0.53587809 1.12548077 1.71508344 2.30468612 2.8942888 3.48389147 4.07349415 4.66309682 5.2526995 2.8942888 3.48389147 4.07349415 5.2526995 -0.64332726 -0.05372458 0.53587809 1.12548077 1.71508344 2.30468612 2.8942888 3.48389147 4.07349415 4.66309682 5.2526995 -0.64332726 -0.05372458 0.53587809 1.12548077 1.71508344 2.30468612 2.8942888 3.48389147 4.07349415 4.66309682 5.2526995 -0.64332726 -0.05372458 0.53587809 1.12548077 1.71508344 2.30468612 2.8942888 3.48389147 4.07349415 4.66309682 5.2526995 -0.64332726 -0.05372458 0.53587809 1.12548077 1.71508344 2.30468612 2.8942888 3.48389147 4.07349415 4.66309682 5.2526995 -0.64332726 -0.05372458 0.53587809 1.12548077 1.71508344 2.30468612 2.8942888 3.48389147 4.07349415 4.66309682 5.2526995 -0.64332726 -0.05372458 0.53587809 1.12548077 1.71508344 2.30468612 2.8942888 3.48389147 4.07349415 4.66309682 5.2526995 -0.64332726 -0.05372458 0.53587809 1.12548077 1.71508344 2.30468612 2.8942888 3.48389147 4.07349415 4.66309682 5.2526995 -0.05372458 0.53587809 1.12548077 1.71508344 2.30468612 2.8942888 3.48389147 4.07349415 4.66309682 5.2526995 -0.64332726 -0.05372458 0.53587809 1.12548077 1.71508344 2.30468612 2.8942888 3.48389147 4.07349415 4.66309682 5.2526995 -0.64332726 -0.05372458 0.53587809 1.12548077 1.71508344 2.30468612 2.8942888 3.48389147 4.07349415 4.66309682 5.2526995 -0.64332726 -0.05372458 0.53587809 1.12548077 1.71508344 2.30468612 2.8942888 3.48389147 4.07349415 4.66309682 -0.64332726 -0.05372458 0.53587809 1.12548077 1.71508344 2.30468612 2.8942888 3.48389147 4.07349415 4.66309682 5.2526995 -0.64332726 -0.05372458 0.53587809 1.12548077 1.71508344 2.30468612 2.8942888 3.48389147 4.07349415 4.66309682 5.2526995 -0.64332726 -0.05372458 0.53587809 1.12548077 1.71508344 2.30468612 2.8942888 3.48389147 4.07349415 4.66309682 5.2526995 -0.64332726 0.53587809 1.12548077 1.71508344 2.30468612 2.8942888 3.48389147 4.07349415 4.66309682 5.2526995 -0.64332726 -0.05372458 0.53587809 1.12548077 1.71508344 2.30468612 4.07349415 4.66309682 5.2526995 -0.05372458 0.53587809 1.12548077 1.71508344 2.30468612 2.8942888 3.48389147 4.07349415 4.66309682 5.2526995 -0.64332726 -0.05372458 0.53587809 1.12548077 1.71508344 2.30468612 2.8942888 3.48389147 4.07349415 4.66309682 5.2526995 -0.64332726 -0.05372458 0.53587809 1.12548077 1.71508344 2.30468612 2.8942888 3.48389147 4.07349415 4.66309682 5.2526995 -0.64332726 -0.05372458 0.53587809 1.12548077 1.71508344 2.30468612 2.8942888 3.48389147 4.07349415 4.66309682 5.2526995 -0.64332726 -0.05372458 0.53587809 1.12548077 1.71508344 2.30468612 2.8942888 3.48389147 4.07349415 4.66309682 5.2526995 -0.64332726] Girls Growth: Actual: [ 0.00000000e+00 -8.33333333e+01 3.00000000e+02 1.25000000e+02 -2.22222222e+01 1.42857143e+01 0.00000000e+00 0.00000000e+00 -1.25000000e+01 0.00000000e+00 4.28571429e+01 -4.89130435e+00 -4.57142857e+00 1.85628743e+01 2.57575758e+01 -9.63855422e+00 -1.51111111e+01 2.46073298e+01 -2.94117647e+00 -1.29870130e+01 2.18905473e+01 8.16326531e-01 -2.60869565e+00 5.35714286e+00 3.72881356e+01 6.17283951e-01 -1.16564417e+01 4.86111111e+00 -1.05960265e+01 2.96296296e+01 1.42857143e+01 2.70000000e+01 -2.75590551e+00 -2.38095238e+00 6.91056911e+00 3.80228137e+00 8.42490842e+00 -5.06756757e+00 9.60854093e+00 1.78571429e+01 9.09090909e+00 -1.46464646e+01 -5.02958580e+00 6.54205607e+00 -3.20000000e+01 5.29411765e+01 3.07692308e+01 -2.94117647e+01 -1.66666667e+01 2.50000000e+01 -1.20000000e+01 -1.36363636e+01 -1.57894737e+01 4.37500000e+01 5.65217391e+01 -9.09090909e+00 -1.00000000e+01 0.00000000e+00 -3.33333333e+01 0.00000000e+00 0.00000000e+00 -1.66666667e+01 2.00000000e+01 6.66666667e+01 2.00000000e+01 8.33333333e+00 -2.60869565e+01 -1.47058824e+01 -6.89655172e+00 1.48148148e+01 3.54838710e+01 2.85714286e+01 2.03703704e+01 -1.53846154e+01 2.90909091e+01 1.69014085e+01 9.63855422e+00 0.00000000e+00 2.20000000e+02 5.00000000e+01 -3.33333333e+01 6.25000000e+00 -2.94117647e+01 3.33333333e+01 5.62500000e+01 1.20000000e+01 1.42857143e+01 -3.12500000e+01 -6.27802691e+00 -2.67942584e+01 -7.84313725e+00 1.13475177e+01 7.00636943e+00 1.84523810e+01 7.53768844e+00 -9.34579439e+00 1.80412371e+01 -1.31004367e+00 1.63716814e+01 1.17424242e+01 1.14124294e+01 -7.09939148e-01 6.12870276e-01 7.61421320e+00 0.00000000e+00 2.45283019e+00 5.61694291e+00 1.74367916e+00 -1.19965724e+00 -3.12228968e+00 5.03978780e+00 -8.08080808e+00 9.89010989e+00 6.75000000e+00 -1.12412178e+01 -3.16622691e+00 -1.00817439e+01 1.03030303e+01 -8.51648352e+00 1.17117117e+01 2.68817204e+00 2.38095238e+01 3.07692308e+01 -1.76470588e+01 -2.14285714e+01 1.36363636e+01 3.20000000e+01 -1.51515152e+01 5.71428571e+01 -2.27272727e+00 3.02325581e+01 0.00000000e+00 0.00000000e+00 -3.33333333e+01 0.00000000e+00 -1.00000000e+01 0.00000000e+00 -2.22222222e+01 0.00000000e+00 0.00000000e+00 -1.42857143e+01 3.33333333e+01 -1.25000000e+01 -2.08791209e+01 -2.77777778e+00 2.28571429e+01 3.48837209e+00 -1.12359551e+00 3.40909091e+00 7.69230769e+00 -4.08163265e+00 1.38297872e+01 -8.41121495e+00 1.22448980e+01 -2.00000000e+01 1.42857143e+01 1.04166667e+01 3.77358491e+00 1.81818182e+00 1.33928571e+01 -1.25984252e+01 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[14.97507311 14.19748287 13.41989263 12.6423024 11.86471216 11.08712192 10.30953169 9.53194145 8.75435121 7.97676098 7.19917074 14.97507311 14.19748287 13.41989263 12.6423024 11.86471216 11.08712192 10.30953169 9.53194145 8.75435121 7.97676098 7.19917074 14.97507311 14.19748287 13.41989263 12.6423024 11.86471216 11.08712192 10.30953169 9.53194145 8.75435121 7.97676098 7.19917074 14.97507311 14.19748287 13.41989263 12.6423024 11.86471216 11.08712192 10.30953169 9.53194145 8.75435121 7.97676098 7.19917074 14.97507311 14.19748287 13.41989263 12.6423024 11.86471216 11.08712192 10.30953169 9.53194145 8.75435121 7.97676098 7.19917074 14.97507311 14.19748287 13.41989263 12.6423024 11.86471216 11.08712192 10.30953169 9.53194145 8.75435121 7.97676098 7.19917074 14.97507311 14.19748287 13.41989263 12.6423024 11.86471216 11.08712192 10.30953169 9.53194145 8.75435121 7.97676098 7.19917074 14.97507311 14.19748287 13.41989263 12.6423024 11.86471216 11.08712192 10.30953169 9.53194145 8.75435121 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14.19748287 13.41989263 12.6423024 11.86471216 11.08712192 10.30953169 9.53194145 8.75435121 7.97676098 7.19917074 14.97507311 14.19748287 13.41989263 12.6423024 11.86471216 11.08712192 10.30953169 9.53194145 8.75435121 7.97676098 7.19917074 14.97507311 14.19748287 13.41989263 12.6423024 11.86471216 11.08712192 10.30953169 9.53194145 8.75435121 7.97676098 7.19917074 14.97507311] Age Group: 13-14 Boys Growth: Actual: [-1.19266055e+01 1.66666667e+01 0.00000000e+00 0.00000000e+00 4.46428571e+00 -1.70940171e+01 -1.54639175e+01 1.70731707e+01 -5.20833333e+00 -3.29670330e+00 7.95454545e+00 -1.54028436e+00 4.33212996e+00 -6.92041522e-01 -5.80720093e-01 -2.16121495e+00 1.19402985e+00 -3.53982301e+00 -6.72782875e-01 -8.55911330e+00 4.84848485e+00 2.56904303e-01 7.40210124e+00 1.42285460e+00 3.28803157e+00 -1.06112054e+00 -2.14500215e-01 -1.24677558e+00 -6.79146713e+00 -4.06352172e+00 -5.84225901e+00 -1.65460186e+00 1.05152471e+00 3.94839719e+00 8.08574652e+00 7.09812109e+00 1.01039636e+01 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3.20707071e+01 5.73613767e-01 -2.85171103e+00 1.07632094e+01 5.83038869e+00 -1.16861436e+00 -6.75675676e-01 5.61224490e+00 -6.76328502e+00 -4.81565087e+00 -1.97628458e+00 7.41935484e+00 2.85285285e+00 -5.83941606e-01 7.34214391e-02 -4.32868672e+00 -6.90184049e-01 7.79922780e+00 0.00000000e+00 4.94269341e+00 -1.35135135e+00 -1.41552511e+01 -7.97872340e+00 -1.15606936e+00 2.51461988e+01 2.33644860e+00 1.14155251e+01 -4.91803279e+00 4.74137931e+00 5.76131687e+00 1.16731518e+00 8.69565217e+00 -6.00000000e+00 0.00000000e+00 -9.57446809e+00 -9.41176471e+00 2.33766234e+01 6.31578947e+00 -5.94059406e+00 -2.31578947e+01 1.23287671e+01 3.65853659e+00 5.79710145e+00 -6.84931507e+00 1.76470588e+01 6.25000000e+00 -1.17647059e+00 -1.54761905e+01 1.83098592e+01 -7.14285714e+00 -1.28205128e+00 2.20779221e+01 -9.57446809e+00 -9.94475138e+00 -6.13496933e-01 4.07407407e+00 -4.15183867e+00 2.47524752e-01 4.07407407e+00 4.50771056e+00 3.97275823e+00 -2.18340611e-01 1.07221007e+01 4.64426877e+00 -6.83229814e+00 -6.00000000e+00 1.09929078e+01 1.59744409e+00 1.16352201e+01 4.22535211e+00 -6.75675676e+00 5.79710145e-01 1.64265130e+01 7.42574257e-01 5.15970516e+00 -3.09859155e+01 -8.16326531e+00 -1.11111111e+00 2.02247191e+01 1.30841121e+01 4.95867769e+00 1.36752137e+01 1.50442478e+01 -2.30769231e+00 1.11111111e+01 5.50000000e+01 3.22580645e+00 0.00000000e+00 -6.25000000e+00 0.00000000e+00 -3.66666667e+01 5.26315789e+01 6.89655172e+00 -2.25806452e+01 3.75000000e+01 0.00000000e+00 1.89189189e+01 6.36363636e+01 1.38888889e+00 -1.78082192e+01 1.66666667e+00 1.96721311e+01 2.73972603e+00 -2.00000000e+01 2.00000000e+01 2.91666667e+01 5.89005236e+00 -2.59579728e+00 3.04568528e+00 1.47783251e+00 1.45631068e+00 1.01674641e+01 4.56026059e+00 8.41121495e+00 -1.05363985e+00 6.96999032e+00 9.59276018e+00 -7.01754386e-01 2.12014134e+00 8.99653979e+00 -9.20634921e+00 -3.84615385e+00 -3.63636364e-01 -3.64963504e-01 -1.46520147e+00 -7.06319703e+00 -1.60000000e+00 -4.47154472e+00 -5.48387097e+01 1.42857143e+01 -5.00000000e+01 2.00000000e+02 -2.50000000e+01 -1.11111111e+01 1.25000000e+01 5.55555556e+00 -1.17647059e+01 4.00000000e+01 -2.10280374e+00 5.72792363e+00 4.51467269e-01 -2.69662921e+00 3.23325635e+00 4.47427293e-01 1.33630290e+00 -3.95604396e+00 -1.07551487e+01 6.66666667e+00 -1.56250000e+00 -6.59574468e+01 -2.50000000e+01 -8.33333333e+00 2.72727273e+01 0.00000000e+00 -7.14285714e+00 6.15384615e+01 -4.28571429e+01 8.33333333e+01 1.81818182e+01 -2.30769231e+01 5.95238095e-01 7.69230769e+00 -1.46520147e+00 -5.57620818e+00 3.93700787e-01 3.33333333e+00 -3.41555977e+00 9.03732809e+00 -7.38738739e+00 1.30350195e+01 1.06712565e+01 -5.88235294e+00 8.92857143e+00 5.73770492e+00 0.00000000e+00 -1.24031008e+01 7.07964602e+00 1.15702479e+01 -2.22222222e+00 -7.57575758e-01 2.21374046e+01 3.75000000e+00 2.56410256e+01 0.00000000e+00 0.00000000e+00 2.38095238e+00 -5.98006645e+00 3.53356890e-01 1.47887324e+01 3.06748466e+00 5.95238095e-01 1.53846154e+01 -1.02564103e+01 -3.92156863e+00] Predicted: [1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 2.91378104 3.14175864 3.36973625 3.59771385 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.36973625 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302 2.00187063 2.22984823 2.45782583 2.68580344 2.91378104 3.14175864 3.36973625 3.59771385 3.82569145 4.05366906 1.77389302] Girls Growth: Actual: [ 5.00000000e+02 0.00000000e+00 1.66666667e+01 -1.42857143e+01 8.33333333e+01 -9.09090909e+00 0.00000000e+00 0.00000000e+00 -2.00000000e+01 5.00000000e+01 2.50000000e+01 -1.60427807e+01 3.37579618e+01 8.57142857e+00 -1.88596491e+01 7.02702703e+00 1.41414141e+01 -9.73451327e+00 1.37254902e+01 -1.29310345e+01 5.44554455e+00 1.12676056e+01 1.66666667e+01 9.71428571e+00 1.40625000e+01 -2.28310502e+00 -1.30841121e+01 8.60215054e+00 1.93069307e+01 1.32780083e+01 -7.32600733e+00 4.74308300e+00 -2.26415094e+00 7.98611111e+00 3.53697749e+00 0.00000000e+00 8.69565217e+00 7.42857143e+00 9.04255319e+00 3.17073171e+00 -1.32387707e+01 -4.08719346e+00 7.10227273e+00 7.95755968e-01 2.08333333e+01 -3.44827586e+00 7.14285714e+00 2.66666667e+01 -2.10526316e+01 -6.66666667e+00 3.57142857e+00 -3.44827586e+00 3.92857143e+01 3.58974359e+01 1.50943396e+01 -1.00000000e+01 -1.11111111e+01 0.00000000e+00 3.75000000e+01 -2.72727273e+01 -3.75000000e+01 2.00000000e+02 1.33333333e+01 -5.88235294e+00 2.50000000e+01 1.50000000e+01 -4.70588235e+01 3.70370370e+01 2.43243243e+01 3.91304348e+01 6.25000000e+00 -1.47058824e+00 1.34328358e+01 2.89473684e+01 0.00000000e+00 2.44897959e+01 2.29508197e+01 3.33333333e+01 -1.50000000e+01 3.52941176e+01 -8.69565217e+00 -1.42857143e+01 7.22222222e+01 6.45161290e+00 -3.03030303e+00 -6.25000000e+00 3.33333333e+01 -7.50000000e+00 -1.82767624e+00 6.64893617e+00 4.98753117e-01 -4.46650124e+00 -7.79220779e-01 0.00000000e+00 -2.61780105e-01 3.67454068e+00 1.72151899e+01 1.61987041e+01 2.60223048e+00 -4.00320256e-01 8.27974277e+00 5.12249443e+00 2.11864407e+00 0.00000000e+00 1.03734440e+00 -5.47570157e-01 -2.89057123e+00 3.54358611e-01 3.74293785e+00 1.36147039e+00 5.01193317e+00 6.59090909e+00 -8.52878465e-01 -6.02150538e+00 -2.05949657e+00 9.34579439e-01 0.00000000e+00 6.71296296e+00 -1.38828633e+01 1.48614610e+01 8.11403509e+00 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1.16796440e+00 5.82191781e+00 2.26537217e+00 2.84810127e+00 2.46153846e+00 8.10810811e+00 6.38888889e+00 7.83289817e-01 -1.01036269e+01 5.47550432e+00 7.92349727e+00 2.78481013e+00 -1.92307692e+00 1.04575163e+01 -8.87573964e+00 -1.03896104e+01 1.59420290e+01 1.18750000e+01 -1.50837989e+01 -3.28947368e+00 -3.40136054e+00 0.00000000e+00 -5.63380282e+00 -1.07784431e+01 8.05369128e+00 -7.45341615e+00 -4.02684564e+00 -6.99300699e+00 1.12781955e+01 -6.08108108e+00 -1.07913669e+01 -8.06451613e-01 1.21951220e+01 5.07246377e+00 -1.45454545e+01 -1.91489362e+01 -5.26315789e+00 -2.77777778e+00 -4.28571429e+00 -2.98507463e+00 2.00000000e+01 -1.28205128e+00 -1.29870130e+01 1.49253731e+01 0.00000000e+00 -7.60869565e+00 -6.47058824e+00 9.43396226e+00 5.74712644e+00 -1.19565217e+01 -1.23456790e+00 -5.62500000e+00 4.63576159e+00 -1.13924051e+01 8.57142857e+00 9.86842105e+00 9.43089431e+00 -2.82317979e+00 -1.52905199e+00 1.24223602e+00 7.36196319e+00 -1.71428571e+00 -5.08720930e+00 -2.60336907e+00 -6.28930818e-01 1.06012658e+01 1.85979971e+00 -1.26984127e+01 1.81818182e+01 6.15384615e+00 -1.01449275e+01 1.61290323e+00 1.26984127e+01 -1.26760563e+01 -3.22580645e+00 5.00000000e+00 4.44444444e+01 -5.49450549e+00 8.10810811e+00 -5.00000000e+00 2.63157895e+01 9.89583333e+00 -9.95260664e+00 -1.31578947e+01 1.21212121e+00 8.98203593e+00 1.97802198e+01 5.50458716e+00 -8.69565217e+00 -1.63934426e+01 1.37254902e+01 8.62068966e+00 1.11111111e+01 0.00000000e+00 0.00000000e+00 -5.71428571e+00 3.03030303e+00 7.35294118e+00 1.64383562e+01 -2.35294118e+00 -1.30434783e+01 -3.25000000e+01 2.59259259e+01 8.82352941e+00 2.70270270e+01 2.12765957e+00 4.16666667e+00 8.00000000e+00 -1.85185185e+00 1.69811321e+01 1.12903226e+01 -2.27272727e+00 -5.42635659e+00 -1.63934426e+00 -7.50000000e+00 1.80180180e+01 9.92366412e+00 -6.25000000e+00 5.18518519e+00 -3.52112676e+00 -2.91970803e+00 1.87969925e+01 1.69491525e+01 1.15942029e+01 4.54545455e+00 2.48447205e+00 1.63636364e+01 9.89583333e+00 1.37440758e+01 7.08333333e+00 -1.43968872e+01 4.90909091e+01 -1.82926829e+00 -3.33333333e+01 -5.00000000e+01 3.33333333e+01 5.00000000e+01 0.00000000e+00 1.66666667e+01 1.00000000e+02 1.00000000e+02 0.00000000e+00 3.92857143e+01 2.56410256e+01 2.22222222e+01 -9.09090909e+00 -4.00000000e+01 1.33333333e+02 1.42857143e+01 4.37500000e+01 4.78260870e+01 -2.05882353e+01 1.11111111e+01 1.66666667e+01 -8.57142857e+00 -3.07692308e+01 1.11111111e+01 3.00000000e+01 2.69230769e+01 1.51515152e+01 1.31578947e+01 2.09302326e+01 -9.61538462e-01 -9.70873786e-01 4.50980392e+01 -1.35135135e+01 3.33333333e+00 -2.25806452e+01 6.66666667e+01 1.75000000e+01 1.27659574e+01 1.13207547e+01 2.54237288e+01 5.40540541e+00 2.56410256e+00 1.87500000e+01 3.15789474e+00 -1.03825137e+01 6.09756098e-01 6.06060606e+00 1.65714286e+01 3.92156863e+00 -1.27358491e+01 8.64864865e+00 2.48756219e+01 4.78087649e+00 -7.98479087e+00 1.11570248e+01 4.44444444e+00 8.51063830e+00 -9.80392157e+00 1.30434783e+01 1.92307692e+00 1.13207547e+01 1.52542373e+01 -8.82352941e+00 6.45161290e+00 2.42424242e+01 -2.07317073e+01 4.28571429e+01 -1.50000000e+01 1.17647059e+01 -5.26315789e+00 1.66666667e+01 -4.76190476e+00 0.00000000e+00 1.00000000e+01 -4.54545455e+00 1.42857143e+01 2.08333333e+01 3.00000000e+02 -6.66666667e+01 1.50000000e+02 0.00000000e+00 -4.00000000e+01 1.66666667e+02 3.75000000e+01 -5.45454545e+01 8.00000000e+01 1.11111111e+01 -8.62068966e+00 -1.88679245e+00 7.69230769e+00 1.96428571e+01 1.64179104e+01 -1.02564103e+01 1.14285714e+01 1.41025641e+01 0.00000000e+00 4.04494382e+01 1.36000000e+01 1.25000000e+01 -2.88888889e+01 -1.25000000e+01 8.21428571e+01 1.17647059e+01 -1.75438596e+01 -1.91489362e+01 2.63157895e+01 1.87500000e+01 -7.01754386e+00 0.00000000e+00 -1.81818182e+01 -3.33333333e+01 -1.66666667e+01 0.00000000e+00 1.20000000e+02 9.09090909e+00 2.50000000e+01 -4.66666667e+01 6.25000000e+01 6.92307692e+01 9.09090909e+00 -1.00000000e+02 -1.00000000e+02 0.00000000e+00 6.00000000e+02 -2.85714286e+01 9.09090909e+00 -8.33333333e+00 -9.09090909e+00 0.00000000e+00 7.00000000e+01 5.29411765e+01 -1.15384615e+01 -3.91304348e+01 6.42857143e+01 1.30434783e+01 3.84615385e+00 2.05882353e+01 9.75609756e+00 1.11111111e+01 5.00000000e+01 6.66666667e+00 5.00000000e+00 3.57142857e+00 2.75862069e+01 -9.00900901e-01 1.45454545e+01 -2.38095238e+00 5.55555556e+01 -4.28571429e+01 1.00000000e+02 2.50000000e+01 0.00000000e+00 -5.00000000e+00 -5.26315789e+00 1.11111111e+01 2.00000000e+01 8.33333333e+00 -3.07692308e+01 0.00000000e+00 6.66666667e+01 -2.00000000e+01 -7.50000000e+01 0.00000000e+00 2.50000000e+01 2.17391304e+01 7.14285714e-01 1.77304965e+01 -7.83132530e+00 -1.04575163e+01 1.60583942e+01 1.06918239e+01 -4.54545455e+00 8.33333333e+00 7.14285714e+00 5.00000000e+01 -1.00000000e+02 -1.00000000e+02 -3.33333333e+01 0.00000000e+00 -2.17391304e+00 -1.77777778e+01 -2.70270270e+00 4.44444444e+01 2.30769231e+01 -1.71875000e+01 1.88679245e+01 0.00000000e+00 -1.11111111e+01 5.00000000e+01 1.19047619e+01 -3.33333333e+01 2.50000000e+01 -2.00000000e+01 -5.00000000e+01 4.50000000e+02 9.09090909e+00 5.00000000e+01 -5.55555556e+00 -5.88235294e+00 2.50000000e+01 4.00000000e+01 1.33333333e+01 0.00000000e+00 1.17647059e+01 -1.57894737e+01 -6.25000000e+00 8.00000000e+01 -2.59259259e+01 1.50000000e+01 0.00000000e+00 8.69565217e+00 2.80000000e+01 7.14285714e+00 -1.33333333e+00 5.40540541e+00 1.41025641e+01 1.57303371e+01 -2.13592233e+01 1.97530864e+01 2.06185567e+00 1.31313131e+01 3.03571429e+01 -4.10958904e+00 1.14636097e+00 3.40010543e+00 2.52357889e+00 2.95872700e+00 1.61796667e+00 1.24762357e+00 1.61952822e+00 1.54752281e+00 2.46787217e+00 9.24528302e+00 3.07832978e+00] Predicted: [ 8.06933817 8.45077893 8.8322197 9.21366046 9.59510123 9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581 8.06933817 8.45077893 8.8322197 9.21366046 9.59510123 9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581 8.06933817 8.45077893 8.8322197 9.21366046 9.59510123 9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581 8.06933817 8.45077893 8.8322197 9.21366046 9.59510123 9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581 8.06933817 8.45077893 8.8322197 9.21366046 9.59510123 9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581 8.06933817 8.45077893 8.8322197 9.21366046 9.59510123 9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581 8.06933817 8.45077893 8.8322197 9.21366046 9.59510123 9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581 8.06933817 8.45077893 8.8322197 9.21366046 9.59510123 9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581 8.06933817 8.45077893 8.8322197 9.21366046 9.59510123 9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581 8.06933817 8.45077893 8.8322197 9.21366046 9.59510123 9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581 8.06933817 8.45077893 8.8322197 9.21366046 9.59510123 9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581 8.06933817 8.45077893 8.8322197 9.21366046 9.59510123 9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581 8.06933817 8.45077893 8.8322197 9.21366046 9.59510123 9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581 8.06933817 8.45077893 8.8322197 9.21366046 9.59510123 9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581 8.06933817 8.45077893 8.8322197 9.21366046 9.59510123 9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581 8.06933817 8.45077893 8.8322197 9.21366046 9.59510123 9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581 8.06933817 8.45077893 8.8322197 9.21366046 9.59510123 9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581 8.06933817 8.45077893 8.8322197 9.21366046 9.59510123 9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581 8.06933817 8.45077893 8.8322197 9.21366046 9.59510123 9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581 8.06933817 8.45077893 8.8322197 9.21366046 9.59510123 9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581 8.06933817 8.45077893 8.8322197 9.21366046 9.59510123 9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581 8.06933817 8.45077893 8.8322197 9.21366046 9.59510123 9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581 8.06933817 8.45077893 8.8322197 9.21366046 9.59510123 9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581 8.06933817 8.45077893 8.8322197 9.21366046 9.59510123 9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581 8.06933817 8.45077893 8.8322197 9.21366046 9.59510123 9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581 8.06933817 8.45077893 8.8322197 9.21366046 9.59510123 9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581 8.06933817 8.45077893 8.8322197 9.21366046 9.59510123 9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581 8.06933817 8.45077893 8.8322197 9.21366046 9.59510123 9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581 8.06933817 8.45077893 8.8322197 9.21366046 9.59510123 9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581 9.97654199 10.35798275 10.73942352 11.12086428 11.88374581 8.06933817 8.45077893 8.8322197 9.21366046 9.59510123 9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581 8.06933817 8.45077893 8.8322197 9.21366046 9.59510123 9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581 8.06933817 8.45077893 8.8322197 9.21366046 9.59510123 9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581 8.06933817 8.45077893 8.8322197 9.21366046 9.59510123 9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581 8.06933817 8.45077893 8.8322197 9.21366046 9.59510123 9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581 8.06933817 8.45077893 8.8322197 9.21366046 9.59510123 9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581 8.06933817 8.45077893 8.8322197 9.21366046 9.59510123 9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581 8.06933817 8.45077893 8.8322197 9.21366046 9.59510123 9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581 8.06933817 8.45077893 8.8322197 9.21366046 9.59510123 9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581 8.06933817 8.45077893 8.8322197 9.21366046 9.59510123 9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581 8.06933817 8.45077893 8.8322197 9.21366046 9.59510123 9.97654199 10.73942352 11.50230505 11.88374581 8.06933817 8.45077893 8.8322197 9.21366046 9.59510123 9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581 8.06933817 8.45077893 8.8322197 9.21366046 9.59510123 9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581 8.06933817 8.45077893 8.8322197 9.21366046 9.59510123 9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581 8.06933817 8.45077893 8.8322197 9.21366046 9.59510123 9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581 8.06933817 8.45077893 8.8322197 9.21366046 9.59510123 9.97654199 10.35798275 10.73942352 11.50230505 11.88374581 8.06933817 8.45077893 8.8322197 9.21366046 9.59510123 9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581 8.06933817 8.45077893 8.8322197 9.21366046 9.59510123 9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581 8.06933817 8.45077893 8.8322197 9.21366046 9.59510123 9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581 8.06933817 8.45077893 8.8322197 9.21366046 9.59510123 9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581 8.06933817 8.45077893 8.8322197 9.21366046 9.59510123 9.97654199 10.35798275 10.73942352 11.12086428 11.50230505 11.88374581 8.06933817] Age Group: 11-12 Boys Growth: Actual: [-1.80180180e+01 1.09890110e+01 4.95049505e+00 0.00000000e+00 -1.79245283e+01 3.44827586e+00 -1.11111111e+00 3.37078652e+00 1.08695652e+00 1.07526882e+01 3.88349515e+00 0.00000000e+00 -1.15807759e-01 -4.34782609e+00 1.75757576e+00 -1.60810006e+00 -1.21065375e+00 -5.20833333e+00 -1.03425986e+00 -3.46178968e+00 6.08930988e-01 -2.35373235e+00 -5.24344569e+00 1.14185332e+00 1.73686496e+00 9.38967136e-01 -5.28541226e+00 -3.30357143e+00 9.69529086e-01 -5.98994056e+00 -6.66342412e+00 -5.21104742e-02 -3.75391032e+00 6.81051922e+00 1.22159091e+01 6.89170183e+00 1.31578947e+00 7.79220779e-01 -3.09278351e+00 -3.16489362e+00 -3.29579786e+00 -1.04515763e+01 -4.31335236e+00 -9.77792509e+00 -1.72910663e+00 -3.51906158e+00 1.18541033e+01 0.00000000e+00 5.16304348e+00 4.39276486e+00 -4.45544554e+00 1.03626943e+00 2.56410256e-01 7.16112532e+00 -3.57995227e+00 -9.52380952e+00 -6.57894737e+00 3.52112676e+00 -2.04081633e+00 -2.08333333e+00 -7.09219858e+00 -5.34351145e+00 3.22580645e+00 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-6.81431005e-01 4.97427101e+00 -8.49673203e+00 6.07142857e+00 -1.85185185e+00 0.00000000e+00 -2.57575758e+01 3.87755102e+01 7.35294118e-01 -1.60583942e+01 -1.47826087e+01 4.59183673e+01 -1.25874126e+01 -7.20000000e+00 2.50000000e+01 1.37931034e+01 -1.50564617e+00 1.33757962e+00 2.76555625e+00 -2.01834862e+00 1.62297129e+00 -1.04422604e+00 2.48292986e+00 2.42277408e+00 -2.95683028e+00 5.11882998e+00 8.69565217e+00 2.41423126e+00 -5.64516129e+00 3.02432610e+00 5.42437779e+00 -5.69007264e+00 2.56739409e+00 -8.13516896e-01 -3.72239748e+00 -1.96592398e-01 1.18187787e+00 -2.92018170e+00 1.36986301e+01 2.16867470e+01 2.97029703e+00 -1.63461538e+01 2.29885057e+01 1.68224299e+01 -1.52000000e+01 -1.13207547e+01 -4.25531915e+00 3.33333333e+00 1.18279570e+01 -1.03363413e+00 -6.63129973e-02 8.46051758e-01 1.00345452e+00 -1.23778502e+00 1.36873351e+00 1.95217179e-01 -1.47751258e+00 3.46077785e-01 -1.64230580e-01 -2.13850962e-01 -4.16922134e+00 -5.11836212e-01 2.05787781e+00 4.03276623e+00 -2.18049667e+00 -8.66873065e-01 -5.37164272e+00 -3.89438944e+00 -6.04395604e+00 2.19298246e-01 -2.62582057e+00 -8.99742931e-01 -9.98702983e+00 -7.20461095e-01 3.62844702e+00 -4.20168067e+00 -4.23976608e+00 -5.64885496e+00 -9.54692557e+00 -9.83899821e+00 7.14285714e+00 0.00000000e+00 -5.20231214e+00 -6.46341463e+00 4.82398957e+00 -3.98009950e+00 1.29533679e+00 -3.58056266e+00 -7.95755968e-01 -1.20320856e+00 -1.13667118e+01 2.44274809e+00 -6.85543964e+00 4.38756856e+00 -2.97723292e+00 1.80505415e+00 -6.73758865e+00 -2.66159696e+00 -5.66406250e+00 -2.48447205e+00 -3.82165605e+00 -1.58940397e+01 2.36220472e+00 -3.33333333e+00 4.44964871e+00 -8.74439462e+00 1.71990172e+00 6.76328502e+00 1.58371041e+00 -7.12694878e+00 -1.00719424e+01 -2.93333333e+00 1.64835165e+00 6.21621622e+00 1.52671756e+00 -2.33352305e+00 1.94250194e-02 2.71897456e-01 2.16928143e+00 -2.46445498e-01 -7.06955530e+00 -1.49284254e+00 -5.60514843e-01 -1.11691023e+01 7.35605170e+00 1.02889667e+00 5.33980583e+00 -8.75576037e+00 8.08080808e+00 9.34579439e+00 1.45299145e+01 -3.35820896e+00 -2.31660232e+00 1.26482213e+01 5.61403509e+00 1.96013289e+01 -1.38888889e+00 -4.62519936e+00 1.05351171e+01 7.71558245e+00 3.23033708e+00 6.53061224e+00 2.55427842e-01 1.65605096e+00 5.13784461e+00 -1.43027414e+00 4.83675937e+00 1.96078431e+00 8.66425993e+00 3.32225914e-01 -4.63576159e+00 4.16666667e+00 3.33333333e+00 7.74193548e+00 2.99401198e+00 -1.16279070e+00 -4.11764706e+00 -2.45398773e+00 3.14465409e+00 1.46341463e+01 -4.78723404e+00 1.56424581e+01 -6.76328502e+00 -6.21761658e+00 2.09944751e+01 -9.13242009e-01 9.67741935e+00 -6.30252101e+00 -2.24215247e+00 5.50458716e+00 9.80392157e-01 2.91262136e+00 3.77358491e-01 5.26315789e+00 -5.35714286e-01 -7.18132855e-01 -1.80831826e+00 -2.39410681e+00 -3.58490566e+00 4.69667319e+00 2.80373832e+00 2.89761751e+00 5.00625782e-01 9.09090909e+00 3.02511416e+00 6.42659280e+00 1.26496616e+01 6.60813309e+00 -2.16731686e+00 -3.91670359e+01 4.03495994e+01 6.48676700e+00 1.10000000e+03 2.50000000e+01 -1.33333333e+01 -1.00000000e+02 4.00000000e+02 1.97183099e+01 2.35294118e+00 1.26436782e+01 -1.83673469e+01 1.50000000e+01 1.73913043e+01 7.40740741e+01 5.58510638e+01 2.04778157e+01 4.47592068e+01 -1.76125245e+00 1.84466019e+01 1.06557377e+01 2.14814815e+01 5.48780488e+00 -3.46820809e+00 3.59281437e+00 -8.67052023e+00 1.39240506e+01 -1.55555556e+01 0.00000000e+00 1.38157895e+01 5.11363636e+00 -2.16216216e+00 -3.68324125e-01 3.32717190e+00 1.78890877e-01 2.85714286e+00 9.02777778e+00 1.36942675e+01 -1.66666667e+01 2.15126050e+01 9.68188105e-01 1.49295775e+01 -3.18627451e+00 2.43037975e+01 1.28309572e+01 4.33212996e+00 3.63321799e+00 6.51085142e+00 -2.50783699e+00 -1.39871383e+01 1.92523364e+01 3.60501567e+00 1.30970724e+00 1.52091255e-01 3.41685649e+00 6.09397944e+00 -6.78200692e+00 2.52412769e+00 7.67559739e+00 -2.55548083e+00 -3.03657695e+00 7.68683274e+00 9.91407799e-01 1.50837989e+01 0.00000000e+00 9.70873786e-01 1.82692308e+01 -3.65853659e+00 -4.21940928e-01 1.77966102e+01 7.19424460e-01 -1.07142857e+00 5.77617329e+00 1.36518771e+00 0.00000000e+00 -1.41732283e+01 -1.00917431e+01 1.22448980e+01 1.54545455e+01 -1.33858268e+01 -9.09090909e-01 -3.66972477e+00 -3.33333333e+01 3.85714286e+01 7.21649485e+00 0.00000000e+00 -3.40206186e+01 4.21875000e+01 0.00000000e+00 4.39560440e+00 4.21052632e+00 1.01010101e+01 9.17431193e+00 -1.68067227e+01 1.91919192e+01 2.03389831e+01 -3.42163355e+00 -3.42857143e+00 3.66863905e+00 -1.48401826e+00 1.07763615e+01 4.39330544e+00 1.40280561e+00 3.16205534e+00 -1.81992337e+00 5.17073171e+00 1.20593692e+00 9.12162162e+00 -8.66873065e+00 8.13559322e+00 -2.19435737e+00 -5.12820513e+00 6.41891892e+00 6.66666667e+00 -1.13095238e+01 -7.04697987e+00 1.69675090e+01 1.11111111e+01 -2.71604938e+01 -2.54237288e+00 2.60869565e+01 2.06896552e+00 -6.75675676e+00 9.42028986e+00 3.97350993e+00 3.18471338e+00 -8.64197531e+00 1.41891892e+01 1.00591716e+01 8.57142857e+00 -1.05263158e+01 -2.35294118e+01 1.53846154e+01 -2.33333333e+01 4.78260870e+01 1.47058824e+01 -4.35897436e+01 1.81818182e+01 1.23076923e+02 0.00000000e+00 1.81818182e+01 1.15384615e+01 -5.74712644e+00 -9.75609756e+00 0.00000000e+00 1.35135135e+01 4.76190476e+00 -7.95454545e+00 4.93827160e+00 1.64705882e+01 -1.81818182e+01 -2.87026406e+00 -1.12293144e+01 7.98934754e-01 1.20211361e+01 6.48584906e+00 8.41638981e+00 3.88151175e+00 3.63815143e+00 4.45920304e+00 1.80744777e+01 3.53846154e+00 -8.61538462e+00 -1.68350168e+00 -2.05479452e+00 -1.74825175e+00 -1.06761566e+00 -6.47482014e+00 3.84615385e+00 -7.77777778e+00 -1.44578313e+01 8.92018779e+00 1.29310345e+00 -7.69230769e+00 1.66666667e+01 -2.50000000e+01 2.85714286e+01 7.40740741e+00 4.48275862e+01 -3.09523810e+01 -1.72413793e+01 1.25000000e+01 0.00000000e+00 1.11111111e+01 -9.90615224e+00 -4.62962963e-01 5.93023256e+00 -4.93962678e+00 5.77367206e-01 -3.32950631e+00 -3.20665083e+00 1.04294479e+01 -1.24444444e+01 -2.03045685e+00 1.02331606e+01 -4.54545455e+01 0.00000000e+00 -4.44444444e+01 5.00000000e+01 3.33333333e+01 2.50000000e+01 -1.20000000e+01 1.81818182e+01 -1.92307692e+01 9.52380952e+00 -8.69565217e+00 -1.37931034e+00 -6.64335664e+00 1.17977528e+01 1.67504188e-01 -8.19397993e+00 2.73224044e+00 -1.77304965e-01 1.01243339e+01 -7.74193548e+00 1.36363636e+01 2.06153846e+01 -1.13821138e+01 4.58715596e+00 2.10526316e+01 -1.52173913e+01 1.62393162e+01 5.14705882e+00 -2.79720280e+00 1.07913669e+01 7.14285714e+00 -7.87878788e+00 7.89473684e+00 -2.02702703e+00 -6.20689655e+00 -3.30882353e+00 1.06463878e+01 1.23711340e+01 7.33944954e+00 6.83760684e+00 1.17333333e+01 -1.40811456e+01 2.22222222e+00 -2.71739130e-01 6.51629073e+00] Predicted: [1.71270382 2.26845394 2.82420406 3.37995418 3.9357043 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 4.49145442 5.04720454 5.60295466 6.15870477 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382 2.26845394 2.82420406 3.37995418 3.9357043 4.49145442 5.04720454 5.60295466 6.15870477 6.71445489 7.27020501 1.71270382] Girls Growth: Actual: [ 2.50000000e+01 -4.00000000e+01 3.33333333e+01 7.50000000e+01 4.28571429e+01 1.00000000e+01 0.00000000e+00 0.00000000e+00 3.63636364e+01 2.66666667e+01 5.26315789e+00 6.17977528e+00 -4.23280423e+00 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8.33333333e+00 -1.53846154e+00 1.01562500e+01 1.41843972e+00 9.79020979e+00 -5.09554140e+00 5.36912752e+00 1.40127389e+01 -5.58659218e-01 2.69662921e+01 -1.32743363e+00 -5.47945205e+00 1.52173913e+01 -8.80503145e+00 4.82758621e+00 -7.89473684e+00 2.85714286e+00 1.66666667e+01 4.76190476e+00 5.68181818e-01 2.31638418e+01 -2.29357798e+00 1.53846154e+01 -6.66666667e+00 -1.42857143e+01 -1.66666667e+01 5.00000000e+01 4.66666667e+01 -1.36363636e+01 -3.15789474e+01 3.84615385e+01 0.00000000e+00 2.77777778e+01 -5.93803787e+00 -2.05855444e+00 -1.40121439e-01 -1.54349860e+00 1.47268409e+00 2.01310861e+00 2.61587884e+00 1.47584973e+00 -7.05156457e-01 5.41500222e+00 3.07368421e+00 8.13008130e-01 3.49462366e+00 4.15584416e+00 5.23690773e+00 -2.84360190e+00 -6.58536585e+00 7.83289817e+00 6.77966102e+00 -9.07029478e+00 0.00000000e+00 -9.97506234e-01 5.61797753e-01 -7.82122905e+00 -6.06060606e-01 7.31707317e+00 7.38636364e+00 -1.21693122e+01 3.01204819e+00 -2.33918129e+00 -2.15568862e+01 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5.26315789e+00 -4.25000000e+01 3.91304348e+01 -1.56250000e+01 1.91489362e+01 8.92857143e+00 2.62295082e+01 1.55844156e+01 6.74157303e+00 1.47368421e+01 1.83486239e+01 1.16279070e+01 -2.36111111e+01 1.36363636e+01 4.88000000e+01 -1.03448276e+01 5.76923077e+01 1.95121951e+01 4.08163265e+00 1.96078431e+00 3.46153846e+01 2.42857143e+01 1.03448276e+01 -8.33333333e+00 1.13636364e+01 1.12244898e+01 4.62427746e+00 8.28729282e+00 5.61224490e+00 -5.31400966e+00 2.55102041e+00 2.63681592e+01 6.69291339e+00 -5.53505535e+00 -7.81250000e+00 2.75423729e+01 1.76079734e+01 -2.74509804e+01 -2.70270270e+00 5.83333333e+01 3.50877193e+00 1.18644068e+01 3.03030303e+00 -2.94117647e+00 1.06060606e+01 -1.09589041e+01 1.53846154e+00 1.66666667e+01 1.17647059e+01 5.26315789e+00 5.00000000e+00 3.33333333e+01 -3.57142857e+00 3.70370370e+00 1.07142857e+01 3.22580645e+00 -2.81250000e+01 3.91304348e+01 1.25000000e+01 -7.00000000e+01 1.50000000e+02 2.00000000e+01 3.33333333e+01 6.25000000e+01 3.84615385e+01 -3.88888889e+01 0.00000000e+00 5.45454545e+01 -5.88235294e+00 -2.57575758e+01 2.85714286e+01 1.74603175e+01 2.02702703e+01 -8.98876404e+00 1.23456790e+00 1.95121951e+01 2.34693878e+01 -1.65289256e+00 3.19327731e+01 1.14649682e+01 -1.17647059e+01 2.33333333e+01 1.08108108e+01 0.00000000e+00 -2.43902439e+01 2.90322581e+01 1.25000000e+01 -2.22222222e+01 2.00000000e+01 2.61904762e+01 1.13207547e+01 -1.25000000e+01 -5.71428571e+01 2.00000000e+02 3.33333333e+01 3.33333333e+01 -1.25000000e+01 5.00000000e+01 1.90476190e+01 -3.20000000e+01 4.70588235e+01 1.60000000e+01 -3.33333333e+01 0.00000000e+00 0.00000000e+00 1.66666667e+02 -1.25000000e+01 -2.85714286e+01 4.00000000e+01 -3.33333333e+01 8.33333333e+00 5.38461538e+01 4.50000000e+01 3.44827586e+00 -3.33333333e+01 3.00000000e+01 2.69230769e+01 -1.81818182e+01 3.70370370e+00 1.42857143e+01 2.24489796e+01 -1.66666667e+00 8.47457627e+00 2.65625000e+01 7.40740741e+00 -1.14942529e+01 1.68831169e+01 2.00000000e+01 7.40740741e+00 2.75862069e+01 2.02702703e+01 1.00000000e+01 0.00000000e+00 -3.63636364e+01 4.28571429e+01 -2.50000000e+01 4.00000000e+01 0.00000000e+00 -9.52380952e+00 5.26315789e+00 2.00000000e+01 8.33333333e+00 1.33333333e+02 -4.28571429e+01 2.50000000e+01 4.00000000e+01 -4.28571429e+01 5.78512397e+00 7.03125000e+00 1.53284672e+01 -1.96202532e+01 5.51181102e+00 1.04477612e+01 9.45945946e+00 8.64197531e+00 -5.11363636e+00 1.19760479e+00 5.91715976e-01 0.00000000e+00 0.00000000e+00 0.00000000e+00 3.00000000e+02 3.33333333e+01 -7.50000000e+01 -4.25531915e+00 2.22222222e+00 1.08695652e+01 1.37254902e+01 -5.17241379e+00 1.81818182e+01 2.15384615e+01 1.77215190e+01 -1.93548387e+01 2.26666667e+01 2.82608696e+01 2.00000000e+02 6.66666667e+01 8.00000000e+01 -3.33333333e+01 6.66666667e+01 7.00000000e+01 5.88235294e+00 0.00000000e+00 5.55555556e+00 3.68421053e+01 -1.53846154e+01 -1.42857143e+01 5.55555556e+00 -2.10526316e+01 -1.33333333e+01 9.23076923e+01 1.60000000e+01 1.03448276e+01 -9.37500000e+00 -6.89655172e+00 4.07407407e+01 -2.63157895e+00 -1.40845070e+00 2.28571429e+01 1.97674419e+01 9.70873786e-01 -9.61538462e+00 2.44680851e+01 2.56410256e+00 3.50000000e+01 -2.22222222e+01 9.52380952e+00 2.89855072e+00 -6.30182421e-01 -6.45304851e-01 4.36730123e+00 2.21030043e+00 1.40667646e+00 1.95652174e+00 5.43202356e+00 3.26463790e+00 -4.13130654e+00 1.17120623e+01 3.85754093e+00] Predicted: [ 6.21696126 6.85823013 7.499499 8.14076786 8.78203673 9.4233056 10.06457446 10.70584333 11.3471122 11.98838107 12.62964993 6.21696126 6.85823013 7.499499 8.14076786 8.78203673 9.4233056 10.06457446 10.70584333 11.3471122 11.98838107 12.62964993 6.21696126 6.85823013 7.499499 8.14076786 8.78203673 9.4233056 10.06457446 10.70584333 11.3471122 11.98838107 12.62964993 6.21696126 6.85823013 7.499499 8.14076786 8.78203673 9.4233056 10.06457446 10.70584333 11.3471122 11.98838107 12.62964993 6.21696126 6.85823013 7.499499 8.14076786 8.78203673 9.4233056 10.06457446 10.70584333 11.3471122 11.98838107 12.62964993 6.21696126 6.85823013 7.499499 8.14076786 8.78203673 9.4233056 10.06457446 10.70584333 11.3471122 11.98838107 12.62964993 6.21696126 6.85823013 7.499499 8.14076786 8.78203673 9.4233056 10.06457446 10.70584333 11.3471122 11.98838107 12.62964993 6.21696126 6.85823013 7.499499 8.14076786 8.78203673 9.4233056 10.06457446 10.70584333 11.3471122 11.98838107 12.62964993 6.21696126 6.85823013 7.499499 8.14076786 8.78203673 9.4233056 10.06457446 10.70584333 11.3471122 11.98838107 12.62964993 6.21696126 6.85823013 7.499499 8.14076786 8.78203673 9.4233056 10.06457446 10.70584333 11.3471122 11.98838107 12.62964993 6.21696126 6.85823013 7.499499 8.14076786 8.78203673 9.4233056 10.06457446 10.70584333 11.3471122 11.98838107 12.62964993 6.21696126 6.85823013 7.499499 8.14076786 8.78203673 9.4233056 10.06457446 10.70584333 11.3471122 11.98838107 12.62964993 6.21696126 6.85823013 7.499499 8.14076786 8.78203673 9.4233056 10.06457446 10.70584333 11.3471122 11.98838107 12.62964993 6.21696126 6.85823013 7.499499 8.14076786 8.78203673 9.4233056 10.06457446 10.70584333 11.3471122 11.98838107 12.62964993 6.21696126 6.85823013 7.499499 8.14076786 8.78203673 9.4233056 10.06457446 10.70584333 11.3471122 11.98838107 12.62964993 6.21696126 6.85823013 7.499499 8.14076786 8.78203673 9.4233056 10.06457446 10.70584333 11.3471122 11.98838107 12.62964993 6.21696126 6.85823013 7.499499 8.14076786 8.78203673 9.4233056 10.06457446 10.70584333 11.3471122 11.98838107 12.62964993 6.21696126 6.85823013 7.499499 8.14076786 8.78203673 9.4233056 10.06457446 10.70584333 11.3471122 11.98838107 12.62964993 6.21696126 6.85823013 7.499499 8.14076786 8.78203673 9.4233056 10.06457446 10.70584333 11.3471122 11.98838107 12.62964993 6.21696126 6.85823013 7.499499 8.14076786 8.78203673 9.4233056 10.06457446 10.70584333 11.3471122 11.98838107 12.62964993 6.21696126 6.85823013 7.499499 8.14076786 8.78203673 9.4233056 10.06457446 10.70584333 11.3471122 11.98838107 12.62964993 6.21696126 6.85823013 7.499499 8.14076786 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13.36843538 15.15657851 -2.72485283 -0.9367097 0.85143343 2.63957657 4.4277197 6.21586284 8.00400597 9.79214911 11.58029224 13.36843538 15.15657851 -2.72485283 -0.9367097 0.85143343 2.63957657 4.4277197 6.21586284 8.00400597 9.79214911 11.58029224 13.36843538 15.15657851 -2.72485283 -0.9367097 0.85143343 2.63957657 4.4277197 6.21586284 8.00400597 9.79214911 11.58029224 13.36843538 15.15657851 -2.72485283 -0.9367097 0.85143343 2.63957657 4.4277197 6.21586284 8.00400597 9.79214911 11.58029224 13.36843538 15.15657851 -2.72485283 -0.9367097 0.85143343 2.63957657 4.4277197 6.21586284 8.00400597 9.79214911 11.58029224 13.36843538 15.15657851 -2.72485283 -0.9367097 0.85143343 2.63957657 4.4277197 6.21586284 8.00400597 9.79214911 11.58029224 13.36843538 15.15657851 -2.72485283 -0.9367097 0.85143343 2.63957657 4.4277197 6.21586284 8.00400597 9.79214911 11.58029224 13.36843538 15.15657851 6.21586284 8.00400597 9.79214911 11.58029224 15.15657851 -2.72485283 -0.9367097 0.85143343 2.63957657 4.4277197 6.21586284 8.00400597 9.79214911 11.58029224 13.36843538 15.15657851 -2.72485283 -0.9367097 0.85143343 2.63957657 4.4277197 6.21586284 8.00400597 9.79214911 11.58029224 13.36843538 15.15657851 -2.72485283 -0.9367097 0.85143343 2.63957657 4.4277197 6.21586284 8.00400597 9.79214911 11.58029224 13.36843538 15.15657851 -2.72485283 -0.9367097 0.85143343 2.63957657 4.4277197 6.21586284 8.00400597 9.79214911 11.58029224 13.36843538 15.15657851 -2.72485283 -0.9367097 0.85143343 2.63957657 4.4277197 6.21586284 8.00400597 9.79214911 11.58029224 13.36843538 15.15657851 -2.72485283 -0.9367097 0.85143343 2.63957657 4.4277197 6.21586284 8.00400597 9.79214911 11.58029224 13.36843538 15.15657851 -2.72485283 -0.9367097 0.85143343 2.63957657 4.4277197 6.21586284 8.00400597 9.79214911 11.58029224 13.36843538 15.15657851 -2.72485283 -0.9367097 0.85143343 2.63957657 4.4277197 6.21586284 8.00400597 9.79214911 11.58029224 13.36843538 15.15657851 -2.72485283 -0.9367097 0.85143343 2.63957657 4.4277197 6.21586284 8.00400597 9.79214911 11.58029224 13.36843538 15.15657851 -2.72485283 -0.9367097 0.85143343 2.63957657 4.4277197 6.21586284 8.00400597 9.79214911 11.58029224 13.36843538 15.15657851 -2.72485283 -0.9367097 0.85143343 2.63957657 4.4277197 6.21586284 8.00400597 9.79214911 11.58029224 13.36843538 15.15657851 -2.72485283 -0.9367097 0.85143343 2.63957657 4.4277197 6.21586284 8.00400597 9.79214911 11.58029224 13.36843538 15.15657851 -2.72485283 -0.9367097 0.85143343 2.63957657 4.4277197 6.21586284 8.00400597 9.79214911 11.58029224 13.36843538 15.15657851 -2.72485283 -0.9367097 0.85143343 2.63957657 4.4277197 6.21586284 8.00400597 9.79214911 11.58029224 13.36843538 15.15657851 -2.72485283 -0.9367097 0.85143343 2.63957657 4.4277197 6.21586284 8.00400597 9.79214911 11.58029224 13.36843538 15.15657851 -2.72485283 -0.9367097 0.85143343 2.63957657 4.4277197 6.21586284 8.00400597 9.79214911 11.58029224 13.36843538 15.15657851 -2.72485283 -0.9367097 0.85143343 2.63957657 4.4277197 6.21586284 8.00400597 9.79214911 11.58029224 13.36843538 15.15657851 -2.72485283 -0.9367097 0.85143343 2.63957657 4.4277197 6.21586284 8.00400597 9.79214911 11.58029224 13.36843538 15.15657851 -2.72485283 -0.9367097 0.85143343 2.63957657 4.4277197 6.21586284 8.00400597 9.79214911 11.58029224 13.36843538 15.15657851 -2.72485283 -0.9367097 0.85143343 2.63957657 4.4277197 6.21586284 8.00400597 9.79214911 11.58029224 13.36843538 15.15657851 -2.72485283 -0.9367097 0.85143343 2.63957657 4.4277197 6.21586284 8.00400597 9.79214911 11.58029224 13.36843538 15.15657851 -2.72485283] Girls Growth: Actual: [-3.33333333e+01 1.50000000e+02 8.00000000e+01 -3.33333333e+01 5.00000000e+01 4.44444444e+01 3.07692308e+01 2.94117647e+01 -2.27272727e+01 -5.88235294e+00 1.87500000e+01 -7.81250000e+00 8.47457627e+00 -3.12500000e+00 5.80645161e+01 -1.58163265e+01 7.27272727e+00 9.60451977e+00 4.63917526e+00 -3.94088670e+00 1.02564103e+00 1.06598985e+01 -9.47368421e+00 4.06976744e+00 2.29050279e+01 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1.01694915e+01 1.23076923e+01 -1.38140162e+00 -1.93599818e-01 3.57142857e+00 4.89148397e+00 3.79161853e+00 7.60979559e+00 7.88038368e+00 2.82426778e+00 -9.15564598e+00 1.64706980e+01 2.68407980e+00] Predicted: [ 8.04772499 8.42995631 8.81218763 9.19441895 9.57665027 9.95888159 10.34111291 10.72334423 11.10557555 11.48780687 11.87003819 8.04772499 8.42995631 8.81218763 9.19441895 9.57665027 9.95888159 10.34111291 10.72334423 11.10557555 11.48780687 11.87003819 8.04772499 8.42995631 8.81218763 9.19441895 9.57665027 9.95888159 10.34111291 10.72334423 11.10557555 11.48780687 11.87003819 8.04772499 8.42995631 8.81218763 9.19441895 9.57665027 9.95888159 10.34111291 10.72334423 11.10557555 11.48780687 11.87003819 8.04772499 8.42995631 8.81218763 9.19441895 9.57665027 9.95888159 10.34111291 10.72334423 11.10557555 11.48780687 11.87003819 8.04772499 8.42995631 8.81218763 9.19441895 9.57665027 9.95888159 10.34111291 10.72334423 11.10557555 11.48780687 11.87003819 8.04772499 8.42995631 8.81218763 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9.57665027 9.95888159 10.34111291 10.72334423 11.10557555 11.48780687 11.87003819 8.04772499 8.42995631 8.81218763 9.19441895 9.57665027 9.95888159 10.34111291 10.72334423 11.10557555 11.48780687 11.87003819 8.04772499 8.42995631 8.81218763 9.19441895 9.57665027 9.95888159 10.34111291 10.72334423 11.10557555 11.48780687 11.87003819 8.04772499 8.42995631 8.81218763 9.19441895 9.57665027 9.95888159 10.34111291 10.72334423 11.10557555 11.48780687 11.87003819 8.04772499 8.42995631 8.81218763 9.19441895 9.57665027 9.95888159 10.34111291 10.72334423 11.10557555 11.48780687 11.87003819 8.04772499] Age Group: 7-8 Boys Growth: Actual: [-2.14285714e+01 -1.09090909e+01 2.24489796e+01 0.00000000e+00 3.66666667e+01 1.95121951e+01 -1.02040816e+01 -2.04545455e+01 -2.85714286e+00 2.94117647e+00 1.57142857e+01 -4.77941176e+00 1.06177606e+01 -1.12565445e+01 -2.65486726e+00 1.43434343e+01 1.36042403e+01 -1.32192846e+00 -1.24507486e+01 -1.32313231e+01 5.91286307e+00 5.87659158e+00 -1.74531351e+00 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-2.15355805e+01 4.26014320e+01 7.69874477e+00 -1.60194175e+01 -7.51445087e+00 -6.25000000e+00 2.13333333e+01 -1.20879121e+01 2.25000000e+01 -6.63265306e+00 1.91256831e+01 -2.75229358e+01 9.49367089e+00 -1.38728324e+01 -8.69565217e+00 9.52380952e+00 -8.69565217e+00 8.57142857e+01 1.35897436e+02 -4.45652174e+01 -4.31372549e+01 -1.37931034e+01 -3.60000000e+01 1.87500000e+01 -5.26315789e+00 -6.58082976e+00 -1.04134763e+01 -1.36752137e+00 2.59965338e+00 4.52702703e+01 -1.51162791e+00 1.41676505e+00 -5.58789290e+00 -3.46485820e+01 1.84905660e+01 6.52866242e+00 5.55555556e+00 -2.10526316e+01 1.33333333e+01 2.94117647e+01 1.81818182e+01 4.61538462e+01 -2.89473684e+01 -1.48148148e+01 -2.17391304e+01 -3.33333333e+01 2.50000000e+01 -1.20000000e+01 -2.94117647e+00 8.26446281e+00 7.37913486e+00 9.47867299e+00 2.09956710e+01 1.52057245e+01 9.62732919e+00 -3.92351275e+01 5.40792541e+01 -3.02571861e-01 7.21649485e+00 -1.92307692e+00 1.66666667e+01 -1.68067227e+01 2.52525253e+01 1.04838710e+01 1.89781022e+01 -2.57668712e+01 -1.07438017e+01 6.01851852e+01 5.20231214e+00 6.58436214e+00 -6.56370656e+00 -8.26446281e-01 3.33333333e+01 2.18750000e+01 2.66666667e+01 -7.89473684e+00 0.00000000e+00 -2.08791209e+01 1.11111111e+01 4.75000000e+00 5.12422360e+00] Predicted: [2.21771046 2.49526088 2.77281129 3.0503617 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146 2.21771046 2.49526088 2.77281129 3.0503617 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146 2.21771046 2.49526088 2.77281129 3.0503617 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146 2.21771046 2.49526088 2.77281129 3.0503617 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146 2.21771046 2.49526088 2.77281129 3.0503617 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146 2.21771046 2.49526088 2.77281129 3.0503617 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146 2.21771046 2.49526088 2.77281129 3.0503617 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146 2.21771046 2.49526088 2.77281129 3.0503617 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146 2.21771046 2.49526088 2.77281129 3.0503617 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146 2.21771046 2.49526088 2.77281129 3.0503617 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146 2.21771046 2.49526088 2.77281129 3.0503617 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146 2.21771046 2.49526088 2.77281129 3.0503617 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146 2.21771046 2.49526088 2.77281129 3.0503617 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146 2.21771046 2.49526088 2.77281129 3.0503617 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146 2.21771046 2.49526088 2.77281129 3.0503617 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146 2.21771046 2.49526088 2.77281129 3.0503617 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146 2.21771046 2.49526088 2.77281129 3.0503617 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146 2.21771046 2.49526088 2.77281129 3.0503617 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146 2.21771046 2.49526088 2.77281129 3.0503617 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146 2.21771046 2.49526088 2.77281129 3.0503617 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146 2.21771046 2.49526088 2.77281129 3.0503617 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146 2.21771046 2.49526088 2.77281129 3.0503617 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146 2.21771046 2.49526088 2.77281129 3.0503617 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146 2.21771046 2.49526088 2.77281129 3.0503617 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146 2.21771046 2.49526088 2.77281129 3.0503617 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146 2.21771046 2.49526088 2.77281129 3.0503617 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146 2.21771046 2.49526088 2.77281129 3.0503617 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146 2.21771046 2.49526088 2.77281129 3.0503617 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146 2.21771046 2.49526088 2.77281129 3.0503617 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146 3.60546253 3.88301294 4.16056336 4.43811377 2.21771046 2.49526088 2.77281129 3.0503617 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146 2.21771046 2.49526088 2.77281129 3.0503617 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146 2.21771046 2.49526088 2.77281129 3.0503617 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146 2.21771046 2.49526088 2.77281129 3.0503617 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146 2.21771046 2.49526088 2.77281129 3.0503617 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146 2.21771046 2.49526088 2.77281129 3.0503617 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146 2.21771046 2.49526088 2.77281129 3.0503617 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146 2.21771046 2.49526088 2.77281129 3.0503617 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146 2.21771046 2.49526088 2.77281129 3.0503617 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146 2.21771046 2.49526088 2.77281129 3.0503617 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146 2.21771046 2.49526088 2.77281129 3.0503617 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146 2.21771046 2.49526088 2.77281129 3.0503617 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146 2.21771046 2.49526088 2.77281129 3.0503617 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146 2.21771046 2.49526088 2.77281129 3.0503617 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146 2.21771046 2.49526088 2.77281129 3.0503617 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146 2.21771046 2.49526088 2.77281129 3.0503617 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146 2.21771046 2.49526088 2.77281129 3.0503617 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146 2.21771046 2.49526088 2.77281129 3.0503617 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146 2.21771046 2.49526088 2.77281129 3.0503617 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146 2.21771046 2.49526088 2.77281129 3.0503617 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146 2.21771046 2.49526088 2.77281129 3.0503617 3.32791212 3.60546253 3.88301294 4.16056336 4.43811377 4.71566418 4.9932146 2.21771046] Girls Growth: Actual: [ 1.00000000e+02 0.00000000e+00 0.00000000e+00 7.50000000e+01 1.42857143e+01 1.75000000e+02 -3.18181818e+01 -2.00000000e+01 0.00000000e+00 8.33333333e+00 -2.30769231e+01 -1.03896104e+01 3.62318841e+01 -9.57446809e+00 3.17647059e+01 1.25000000e+01 2.38095238e+01 1.08974359e+01 -7.51445087e+00 6.25000000e+00 1.76470588e+00 -1.32947977e+01 7.09219858e-01 5.63380282e+00 8.00000000e+00 0.00000000e+00 4.25925926e+01 1.55844156e+01 7.49063670e-01 1.59851301e+01 -2.94871795e+01 2.90909091e+01 2.53521127e+01 -2.31023102e+00 6.75675676e-01 -6.71140940e+00 2.87769784e+00 4.19580420e+00 4.93288591e+01 -1.05617978e+01 -1.30653266e+01 -6.93641618e+00 -4.65838509e+00 8.79478827e+00 0.00000000e+00 4.16666667e+00 2.80000000e+01 3.75000000e+01 1.81818182e+01 1.73076923e+01 3.27868852e+00 6.34920635e+00 -1.49253731e+00 9.09090909e+00 2.77777778e+00 -2.14285714e+01 -9.09090909e+00 2.00000000e+01 -3.33333333e+01 7.50000000e+01 -6.42857143e+01 1.60000000e+02 -7.69230769e+00 -8.33333333e+00 2.72727273e+01 2.85714286e+01 3.50877193e+00 0.00000000e+00 3.22033898e+01 3.84615385e+01 -6.48148148e+00 2.17821782e+01 -1.62601626e+00 5.45454545e+01 -2.24598930e+01 2.75862069e+01 9.18918919e+00 7.69230769e+00 -3.57142857e+00 -1.48148148e+01 -8.69565217e+00 8.09523810e+01 -2.36842105e+01 1.03448276e+01 -1.56250000e+01 -1.11111111e+01 3.33333333e+01 9.37500000e+00 -9.45945946e+00 -6.21890547e+00 5.30503979e+00 2.34256927e+01 1.14285714e+01 9.70695971e+00 9.18196995e+00 2.29357798e+00 -1.94319880e+00 2.08841463e+01 2.52206810e+00 -5.17241379e+00 -2.68774704e+00 5.44272949e+00 1.77195686e+00 4.54201363e+00 8.39971035e+00 4.67601870e+00 -6.19017230e+00 -1.56462585e+01 2.60483871e+01 5.43825976e+00 3.35731415e+00 -7.42459397e+00 -5.01253133e-01 1.13350126e+01 1.40271493e+01 5.55555556e+00 5.26315789e+00 6.25000000e+00 -2.28571429e+01 3.02832244e+01 9.69899666e+00 5.00000000e+00 1.42857143e+01 2.08333333e+01 -1.55172414e+01 -1.83673469e+01 3.75000000e+01 2.72727273e+01 -1.42857143e+00 -1.30434783e+01 4.83333333e+01 6.74157303e+00 5.71428571e+01 1.81818182e+01 -3.84615385e+01 5.00000000e+01 -8.33333333e+00 1.09090909e+02 2.60869565e+01 -3.44827586e+01 -2.10526316e+01 6.66666667e+01 -8.00000000e+00 -5.12820513e+00 -1.53153153e+01 1.17021277e+01 9.52380952e+00 2.60869565e+01 1.10344828e+01 1.86335404e+01 1.57068063e+00 -1.34020619e+01 2.38095238e+01 3.07692308e+01 -1.81818182e+00 1.48148148e+01 -5.37634409e-01 -6.48648649e+00 1.21387283e+01 2.47422680e+01 -2.47933884e+00 -1.56779661e+01 -2.16080402e+01 3.33333333e+01 1.05769231e+01 2.30769231e+01 5.62500000e+01 -4.00000000e+00 0.00000000e+00 1.66666667e+01 -1.42857143e+01 -1.25000000e+01 -5.71428571e+01 2.22222222e+01 6.36363636e+01 5.55555556e+00 -4.87077535e+00 -9.40438871e-01 5.22151899e+00 5.46365915e+00 2.75665399e+00 9.85198890e+00 2.06315789e+00 -2.59900990e+00 9.74163490e-01 1.48489933e+01 -5.14974434e+00 -7.50750751e+00 -3.89610390e+00 1.04729730e+01 5.19877676e+00 -2.61627907e+00 -4.17910448e+00 1.30841121e+01 1.04683196e+01 -2.69326683e+01 2.18430034e+01 1.73669468e+01 0.00000000e+00 -7.63358779e+00 2.47933884e+00 5.64516129e+00 1.67938931e+01 -8.49673203e+00 1.57142857e+01 3.02469136e+01 -3.69668246e+01 2.93233083e+01 1.16279070e+01 1.29629630e+01 3.27868852e+00 -6.34920635e+00 8.47457627e+00 3.35937500e+01 -1.16959064e+01 3.97350993e+00 -8.91719745e+00 -1.25874126e+01 3.68000000e+01 2.33918129e+00 -1.62162162e+01 8.60215054e+00 -1.38613861e+01 6.89655172e+00 7.52688172e+00 3.00000000e+00 2.91262136e+00 -2.07547170e+01 -9.52380952e+00 2.10526316e+01 -1.73913043e+01 0.00000000e+00 1.31578947e+01 -5.81395349e-01 -1.16959064e+00 5.91715976e-01 0.00000000e+00 -1.76470588e+00 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19.35209951 8.2614355 9.3705019 10.4795683 11.5886347 12.6977011 13.8067675 14.9158339 16.0249003 17.13396671 18.24303311 19.35209951 8.2614355 9.3705019 10.4795683 11.5886347 12.6977011 13.8067675 14.9158339 16.0249003 17.13396671 18.24303311 19.35209951 8.2614355 9.3705019 10.4795683 11.5886347 12.6977011 13.8067675 14.9158339 16.0249003 17.13396671 18.24303311 19.35209951 8.2614355 9.3705019 10.4795683 11.5886347 12.6977011 13.8067675 14.9158339 16.0249003 17.13396671 18.24303311 19.35209951 8.2614355 9.3705019 10.4795683 11.5886347 12.6977011 13.8067675 14.9158339 16.0249003 17.13396671 18.24303311 19.35209951 8.2614355 9.3705019 10.4795683 11.5886347 12.6977011 13.8067675 14.9158339 16.0249003 17.13396671 18.24303311 19.35209951 8.2614355 9.3705019 10.4795683 11.5886347 12.6977011 13.8067675 14.9158339 16.0249003 17.13396671 18.24303311 19.35209951 8.2614355 9.3705019 10.4795683 11.5886347 12.6977011 13.8067675 14.9158339 16.0249003 17.13396671 18.24303311 19.35209951 8.2614355 9.3705019 10.4795683 11.5886347 12.6977011 13.8067675 14.9158339 16.0249003 17.13396671 18.24303311 19.35209951 8.2614355 9.3705019 10.4795683 11.5886347 12.6977011 13.8067675 14.9158339 16.0249003 17.13396671 18.24303311 19.35209951 8.2614355 9.3705019 10.4795683 11.5886347 12.6977011 13.8067675 14.9158339 16.0249003 17.13396671 18.24303311 19.35209951 8.2614355 9.3705019 10.4795683 11.5886347 12.6977011 13.8067675 14.9158339 16.0249003 17.13396671 18.24303311 19.35209951 8.2614355 9.3705019 10.4795683 11.5886347 12.6977011 13.8067675 14.9158339 16.0249003 17.13396671 18.24303311 19.35209951 8.2614355 9.3705019 10.4795683 11.5886347 12.6977011 13.8067675 14.9158339 16.0249003 17.13396671 18.24303311 19.35209951 8.2614355 9.3705019 10.4795683 11.5886347 12.6977011 13.8067675 14.9158339 16.0249003 17.13396671 18.24303311 19.35209951 8.2614355 9.3705019 10.4795683 11.5886347 12.6977011 13.8067675 14.9158339 16.0249003 17.13396671 18.24303311 19.35209951 8.2614355 9.3705019 10.4795683 11.5886347 12.6977011 13.8067675 14.9158339 16.0249003 17.13396671 18.24303311 19.35209951 8.2614355 9.3705019 10.4795683 11.5886347 12.6977011 13.8067675 14.9158339 16.0249003 17.13396671 18.24303311 19.35209951 8.2614355 9.3705019 10.4795683 11.5886347 12.6977011 13.8067675 14.9158339 16.0249003 17.13396671 18.24303311 19.35209951 8.2614355 9.3705019 10.4795683 11.5886347 12.6977011 13.8067675 14.9158339 16.0249003 17.13396671 18.24303311 19.35209951 8.2614355 9.3705019 10.4795683 11.5886347 12.6977011 13.8067675 14.9158339 16.0249003 17.13396671 18.24303311 19.35209951 8.2614355 9.3705019 10.4795683 11.5886347 12.6977011 13.8067675 14.9158339 16.0249003 17.13396671 18.24303311 19.35209951 8.2614355 9.3705019 10.4795683 11.5886347 12.6977011 13.8067675 14.9158339 16.0249003 17.13396671 18.24303311 19.35209951 8.2614355 9.3705019 10.4795683 11.5886347 12.6977011 13.8067675 14.9158339 16.0249003 17.13396671 18.24303311 19.35209951 8.2614355 9.3705019 10.4795683 11.5886347 12.6977011 13.8067675 14.9158339 16.0249003 17.13396671 18.24303311 19.35209951 8.2614355 9.3705019 10.4795683 11.5886347 12.6977011 13.8067675 14.9158339 16.0249003 17.13396671 18.24303311 19.35209951 8.2614355 9.3705019 10.4795683 11.5886347 12.6977011 13.8067675 14.9158339 16.0249003 17.13396671 18.24303311 19.35209951 8.2614355 9.3705019 10.4795683 11.5886347 12.6977011 13.8067675 14.9158339 16.0249003 17.13396671 18.24303311 19.35209951 13.8067675 14.9158339 16.0249003 17.13396671 8.2614355 9.3705019 10.4795683 11.5886347 12.6977011 13.8067675 14.9158339 16.0249003 17.13396671 18.24303311 19.35209951 8.2614355 9.3705019 10.4795683 11.5886347 12.6977011 13.8067675 14.9158339 16.0249003 17.13396671 18.24303311 19.35209951 8.2614355 9.3705019 10.4795683 11.5886347 12.6977011 13.8067675 14.9158339 16.0249003 17.13396671 18.24303311 19.35209951 8.2614355 9.3705019 10.4795683 11.5886347 12.6977011 13.8067675 14.9158339 16.0249003 17.13396671 18.24303311 19.35209951 8.2614355 9.3705019 10.4795683 11.5886347 12.6977011 13.8067675 14.9158339 16.0249003 17.13396671 18.24303311 19.35209951 8.2614355 9.3705019 10.4795683 11.5886347 12.6977011 13.8067675 14.9158339 16.0249003 17.13396671 18.24303311 19.35209951 8.2614355 9.3705019 10.4795683 11.5886347 12.6977011 13.8067675 14.9158339 16.0249003 17.13396671 18.24303311 19.35209951 8.2614355 9.3705019 10.4795683 11.5886347 12.6977011 13.8067675 14.9158339 16.0249003 17.13396671 18.24303311 19.35209951 8.2614355 9.3705019 10.4795683 11.5886347 12.6977011 13.8067675 14.9158339 16.0249003 17.13396671 18.24303311 19.35209951 8.2614355 9.3705019 10.4795683 11.5886347 12.6977011 13.8067675 14.9158339 16.0249003 17.13396671 18.24303311 19.35209951 8.2614355 9.3705019 10.4795683 11.5886347 12.6977011 13.8067675 14.9158339 16.0249003 17.13396671 18.24303311 19.35209951 8.2614355 9.3705019 10.4795683 11.5886347 12.6977011 13.8067675 14.9158339 16.0249003 17.13396671 18.24303311 19.35209951 8.2614355 9.3705019 10.4795683 11.5886347 12.6977011 13.8067675 14.9158339 16.0249003 17.13396671 18.24303311 19.35209951 8.2614355 9.3705019 10.4795683 11.5886347 12.6977011 13.8067675 14.9158339 16.0249003 17.13396671 18.24303311 19.35209951 8.2614355 9.3705019 10.4795683 11.5886347 12.6977011 13.8067675 14.9158339 16.0249003 17.13396671 18.24303311 19.35209951 8.2614355 9.3705019 10.4795683 11.5886347 12.6977011 13.8067675 14.9158339 16.0249003 17.13396671 18.24303311 19.35209951 8.2614355 9.3705019 10.4795683 11.5886347 12.6977011 13.8067675 14.9158339 16.0249003 17.13396671 18.24303311 19.35209951 8.2614355 9.3705019 10.4795683 11.5886347 12.6977011 13.8067675 14.9158339 16.0249003 17.13396671 18.24303311 19.35209951 8.2614355 9.3705019 10.4795683 11.5886347 12.6977011 13.8067675 14.9158339 16.0249003 17.13396671 18.24303311 19.35209951 8.2614355 9.3705019 10.4795683 11.5886347 12.6977011 13.8067675 14.9158339 16.0249003 17.13396671 18.24303311 19.35209951 8.2614355 9.3705019 10.4795683 11.5886347 12.6977011 13.8067675 14.9158339 16.0249003 17.13396671 18.24303311 19.35209951 8.2614355 ] Age Group: 6&U Boys Growth: Actual: [-1.36363636e+01 4.21052632e+01 1.85185185e+01 -3.12500000e+00 8.38709677e+01 1.40350877e+01 -2.76923077e+01 -1.91489362e+01 -7.89473684e+00 4.00000000e+01 -2.24489796e+01 -2.79720280e+00 2.82374101e+01 -2.21598878e+01 -8.64864865e+00 6.07495069e+01 1.05521472e+01 -3.32963374e-01 -1.87082405e+01 -2.68493151e+01 1.92883895e+01 -1.61695447e+01 -7.16981132e+00 1.19241192e+01 1.69491525e+00 -4.04761905e+00 4.20595533e+01 -4.36681223e-01 2.21929825e+01 -1.06963388e+01 -3.64951768e+01 4.21518987e+01 1.98575245e+01 -9.87996307e+00 3.66290984e+01 -1.43607049e+01 -5.82311734e+00 -1.37610414e+01 2.71159030e+01 -1.92111959e+01 -9.81627297e+00 -2.02561118e+01 5.03649635e+00 8.61709521e+00 -9.54446855e+00 -2.87769784e+00 4.44444444e+00 3.54609929e+00 -1.00456621e+01 4.06091371e+00 4.87804878e+00 -1.53488372e+01 -4.94505495e+00 1.47398844e+01 -1.33501259e+01 -4.64516129e+01 8.43373494e+00 -3.33333333e+00 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-2.80997541e+01 3.43429409e+01 -1.74545455e+00 1.21093750e+01 1.11498258e+01 -6.89655172e+00 6.06060606e+00 -7.61904762e+00 -3.78006873e+00 3.92857143e+00 -1.37457045e+00 0.00000000e+00 2.82229965e+01 2.71739130e-01 2.45098039e-01 2.32273839e+00 4.18160096e+00 2.79816514e+01 8.96057348e-01 -2.13143872e+00 -9.98185118e-01 0.00000000e+00 -7.42438130e+00 3.26732673e+00 -5.75263663e+00 1.60771704e+01 8.31024931e-01 -3.84615385e+00 6.00000000e+00 -6.19946092e+00 6.60919540e+00 -2.15633423e+00 3.30578512e+00 -1.60000000e+00 1.51761518e+01 -1.55294118e+01 0.00000000e+00 -2.09401709e+01 1.18918919e+01 1.69082126e+01 8.26446281e+00 -4.19847328e+00 -1.59362550e+01 -6.16113744e+00 -8.58585859e+00 1.82320442e+01 4.67289720e-01 3.69357045e+00 -2.63852243e-01 1.12433862e+01 4.51843044e+00 -9.89761092e+00 -8.58585859e+00 -2.76243094e+00 4.11931818e+00 -1.56889495e+01 1.90938511e+01 1.35869565e-01 1.05769231e+01 1.49068323e+00 1.39534884e+01 9.34479055e+00 2.41650295e+01 4.34335443e+01 -1.98565913e+00 -1.23804164e+00 -8.21082621e+01 2.11783439e+02 4.41266599e+01 6.00000000e+01 6.25000000e+01 2.30769231e+01 -9.37500000e+01 5.76923077e+00 -9.09090909e+00 6.00000000e+00 -4.90566038e+01 1.48148148e+01 1.45161290e+02 8.15789474e+01 3.33333333e+01 3.26086957e+01 1.47540984e+01 -3.92857143e+01 2.96875000e+01 8.43373494e+00 1.77777778e+01 -1.03773585e+01 -2.10526316e+00 1.39784946e+01 -2.83018868e+00 -2.03883495e+01 -4.26829268e+01 9.36170213e+01 -5.49450549e+00 2.29166667e+00 2.40325866e+01 -7.38916256e+00 3.19148936e+00 8.59106529e+00 3.16455696e+00 1.02760736e+01 -4.31154381e+00 -3.70639535e+01 5.26558891e+01 -5.90015129e+00 -1.54639175e+00 4.71204188e+00 1.62500000e+02 2.28571429e+00 0.00000000e+00 3.20297952e+01 -7.61636107e+00 -1.64885496e+01 -4.22303473e+01 4.93670886e+01 -4.23728814e+00 3.22997416e+00 -1.25156446e-01 -1.87969925e+00 -3.70370370e+00 3.60742706e+01 6.33528265e+00 -7.05774519e+00 -2.56410256e+00 -2.10526316e+01 4.21794872e+01 1.06402164e+01 4.52991453e+01 7.05882353e+00 9.34065934e+00 3.01507538e+00 9.75609756e-01 -1.15942029e+01 6.39344262e+01 1.66666667e+01 -3.45714286e+01 6.15720524e+01 -1.08108108e+00 -2.08333333e+01 -1.05263158e+01 2.94117647e+01 1.02272727e+01 7.21649485e+00 7.69230769e+00 -2.76785714e+01 -2.96296296e+01 1.31578947e+02 -7.57575758e+00 1.06382979e+01 -6.53846154e+01 2.38888889e+02 -2.13114754e+01 1.20833333e+02 9.43396226e+00 -1.89655172e+01 -7.44680851e+00 -2.98850575e+01 3.44262295e+01 1.46341463e+01 9.64630225e+00 6.68621701e+01 -2.63620387e+00 -1.51624549e+01 2.48936170e+01 2.72572402e+00 8.29187396e-01 -1.38157895e+01 -1.69847328e+01 1.70114943e+01 -7.26915521e+00 -7.22222222e+00 -1.61676647e+01 -7.14285714e-01 -1.43884892e+00 2.04379562e+01 -8.48484848e+00 1.32450331e+00 -1.24183007e+01 -2.23880597e+00 3.51145038e+01 1.12994350e+00 -2.44604317e+01 9.52380952e+00 2.60869565e+01 6.89655172e+00 3.93548387e+01 1.89814815e+01 2.72373541e+00 -6.43939394e+00 -3.40080972e+01 3.92638037e+01 -1.76211454e+00 3.18181818e+01 -2.41379310e+01 1.36363636e+01 -1.20000000e+01 1.50000000e+02 -2.72727273e+01 2.75000000e+01 -1.17647059e+01 -3.55555556e+01 2.06896552e+01 0.00000000e+00 -2.15686275e+01 -7.50000000e+00 -1.62162162e+01 1.29032258e+02 4.78873239e+01 -9.52380952e+00 3.57894737e+01 -3.33333333e+01 -6.04651163e+01 1.67647059e+02 -4.06593407e+01 -4.90018149e+00 -1.85114504e+01 -1.07728337e+01 -6.03674541e+00 1.02234637e+02 -3.31491713e+00 2.57142857e+00 1.68523677e+01 -2.89630513e+01 4.98322148e+01 -4.47928331e+00 -2.39263804e+01 -1.45161290e+01 2.83018868e+00 -9.17431193e-01 9.25925926e+00 3.30508475e+01 1.91082803e+00 6.25000000e+00 -4.94117647e+01 4.53488372e+01 -2.00000000e+01 -1.53846154e+01 1.36363636e+01 -8.00000000e+00 9.56521739e+01 6.88888889e+01 -3.68421053e+01 -5.00000000e+01 -4.58333333e+01 -3.07692308e+01 1.11111111e+01 -1.00000000e+01 -2.30769231e+01 -1.10000000e+01 1.20786517e+01 -1.07769424e+01 8.98876404e+01 -2.51479290e+00 -1.48710167e+01 -1.40819964e+01 -4.75103734e+01 3.59683794e+01 2.47093023e+01 -2.41379310e+01 -4.09090909e+01 -1.53846154e+01 1.45454545e+02 3.33333333e+01 -2.77777778e+01 -1.15384615e+01 0.00000000e+00 -3.04347826e+01 5.00000000e+01 -1.25000000e+01 -1.09929078e+01 -8.76494024e+00 1.26637555e+01 2.32558140e+00 2.23484848e+01 5.51083591e+01 -7.98403194e-01 1.75050302e+01 -5.25684932e+01 4.33212996e+01 1.18387909e+01 -7.31707317e+00 3.28947368e+01 1.98019802e+00 -2.13592233e+01 3.20987654e+01 3.27102804e+01 -1.83098592e+01 3.44827586e+00 -3.58333333e+01 5.19480519e+01 5.98290598e+00 -2.47232472e+01 -4.90196078e+00 8.76288660e+00 6.16113744e+00 5.00000000e+01 4.28571429e+01 -2.08333333e-01 -1.46137787e+01 -3.49633252e+01 1.24060150e+01 9.36454849e+00 1.18518519e+01] Predicted: [3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 4.94445872 5.13430497 5.32415122 5.51399748 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747 4.18507372 4.37491997 4.56476622 4.75461247 4.94445872 5.13430497 5.32415122 5.51399748 5.70384373 5.89368998 3.99522747] Girls Growth: Actual: [ 1.00000000e+02 1.50000000e+02 -8.00000000e+01 3.00000000e+02 2.50000000e+02 -2.85714286e+01 -4.00000000e+01 -1.66666667e+01 6.00000000e+01 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-2.28571429e+01 -3.70370370e+00 -3.84615385e+01 4.68750000e+01 2.55319149e+01 3.05555556e+01 4.25531915e+00 1.63265306e+01 7.01754386e+00 6.55737705e+01 1.28712871e+01 -2.80701754e+01 1.34146341e+01 -2.79569892e+01 2.98507463e+01 1.26436782e+01 -7.51031303e+00 8.26446281e+00 3.61080819e+00 1.33200795e+01 1.34984520e+01 7.95599200e+00 3.08262444e+00 1.61777923e+00 -2.19747528e+01 3.36768343e+01 -2.82146161e+00] Predicted: [ 9.92272784 11.3435104 12.76429297 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348 9.92272784 11.3435104 12.76429297 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348 9.92272784 11.3435104 12.76429297 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348 9.92272784 11.3435104 12.76429297 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348 9.92272784 11.3435104 12.76429297 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348 9.92272784 11.3435104 12.76429297 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348 9.92272784 11.3435104 12.76429297 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348 9.92272784 11.3435104 12.76429297 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348 9.92272784 11.3435104 12.76429297 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348 9.92272784 11.3435104 12.76429297 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348 9.92272784 11.3435104 12.76429297 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348 9.92272784 11.3435104 12.76429297 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348 9.92272784 11.3435104 12.76429297 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348 9.92272784 11.3435104 12.76429297 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348 9.92272784 11.3435104 12.76429297 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348 9.92272784 11.3435104 12.76429297 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348 9.92272784 11.3435104 12.76429297 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348 9.92272784 11.3435104 12.76429297 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348 9.92272784 11.3435104 12.76429297 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348 9.92272784 11.3435104 12.76429297 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348 9.92272784 11.3435104 12.76429297 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348 9.92272784 11.3435104 12.76429297 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348 9.92272784 11.3435104 12.76429297 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348 9.92272784 11.3435104 12.76429297 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348 9.92272784 11.3435104 12.76429297 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348 9.92272784 11.3435104 12.76429297 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348 9.92272784 11.3435104 12.76429297 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348 9.92272784 11.3435104 12.76429297 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18.44742322 19.86820579 21.28898835 22.70977091 24.13055348 9.92272784 11.3435104 12.76429297 14.18507553 15.60585809 17.02664066 18.44742322 21.28898835 22.70977091 24.13055348 9.92272784 11.3435104 12.76429297 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348 9.92272784 11.3435104 12.76429297 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348 9.92272784 11.3435104 12.76429297 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348 9.92272784 11.3435104 12.76429297 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348 9.92272784 11.3435104 12.76429297 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348 9.92272784 11.3435104 12.76429297 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348 9.92272784 11.3435104 12.76429297 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348 9.92272784 11.3435104 12.76429297 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348 9.92272784 11.3435104 12.76429297 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348 9.92272784 11.3435104 12.76429297 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348 9.92272784 11.3435104 12.76429297 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348 9.92272784 11.3435104 12.76429297 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348 9.92272784 11.3435104 12.76429297 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348 9.92272784 11.3435104 12.76429297 14.18507553 15.60585809 17.02664066 18.44742322 19.86820579 21.28898835 22.70977091 24.13055348 9.92272784] Age Group: Season_1_Compared Boys Growth: Actual: [2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2017. 2018. 2019. 2020. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011.] Predicted: [2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2015.98921821 2016.9883312 2017.98744419 2018.98655718 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324 2011.99276623 2012.99187923 2013.99099222 2014.99010521 2015.98921821 2016.9883312 2017.98744419 2018.98655718 2019.98567018 2020.98478317 2010.99365324] Girls Growth: Actual: [2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2015. 2016. 2017. 2018. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2016. 2017. 2018. 2020. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021. 2011. 2012. 2013. 2014. 2015. 2016. 2017. 2018. 2019. 2020. 2021.] Predicted: [2013.31352932 2013.84823058 2014.38293183 2014.91763309 2015.45233434 2015.9870356 2016.52173685 2017.05643811 2017.59113936 2018.12584062 2018.66054188 2013.31352932 2013.84823058 2014.38293183 2014.91763309 2015.45233434 2015.9870356 2016.52173685 2017.05643811 2017.59113936 2018.12584062 2018.66054188 2013.31352932 2013.84823058 2014.38293183 2014.91763309 2015.45233434 2015.9870356 2016.52173685 2017.05643811 2017.59113936 2018.12584062 2018.66054188 2013.31352932 2013.84823058 2014.38293183 2014.91763309 2015.45233434 2015.9870356 2016.52173685 2017.05643811 2017.59113936 2018.12584062 2018.66054188 2013.31352932 2013.84823058 2014.38293183 2014.91763309 2015.45233434 2015.9870356 2016.52173685 2017.05643811 2017.59113936 2018.12584062 2018.66054188 2013.31352932 2013.84823058 2014.38293183 2014.91763309 2015.45233434 2015.9870356 2016.52173685 2017.05643811 2017.59113936 2018.12584062 2018.66054188 2013.31352932 2013.84823058 2014.38293183 2014.91763309 2015.45233434 2015.9870356 2016.52173685 2017.05643811 2017.59113936 2018.12584062 2018.66054188 2013.31352932 2013.84823058 2014.38293183 2014.91763309 2015.45233434 2015.9870356 2016.52173685 2017.05643811 2017.59113936 2018.12584062 2018.66054188 2013.31352932 2013.84823058 2014.38293183 2014.91763309 2015.45233434 2015.9870356 2016.52173685 2017.05643811 2017.59113936 2018.12584062 2018.66054188 2013.31352932 2013.84823058 2014.38293183 2014.91763309 2015.45233434 2015.9870356 2016.52173685 2017.05643811 2017.59113936 2018.12584062 2018.66054188 2013.31352932 2013.84823058 2014.38293183 2014.91763309 2015.45233434 2015.9870356 2016.52173685 2017.05643811 2017.59113936 2018.12584062 2018.66054188 2013.31352932 2013.84823058 2014.38293183 2014.91763309 2015.45233434 2015.9870356 2016.52173685 2017.05643811 2017.59113936 2018.12584062 2018.66054188 2013.31352932 2013.84823058 2014.38293183 2014.91763309 2015.45233434 2015.9870356 2016.52173685 2017.05643811 2017.59113936 2018.12584062 2018.66054188 2013.31352932 2013.84823058 2014.38293183 2014.91763309 2015.45233434 2015.9870356 2016.52173685 2017.05643811 2017.59113936 2018.12584062 2018.66054188 2013.31352932 2013.84823058 2014.38293183 2014.91763309 2015.45233434 2015.9870356 2016.52173685 2017.05643811 2017.59113936 2018.12584062 2018.66054188 2013.31352932 2013.84823058 2014.38293183 2014.91763309 2015.45233434 2015.9870356 2016.52173685 2017.05643811 2017.59113936 2018.12584062 2018.66054188 2013.31352932 2013.84823058 2014.38293183 2014.91763309 2015.45233434 2015.9870356 2016.52173685 2017.05643811 2017.59113936 2018.12584062 2018.66054188 2013.31352932 2013.84823058 2014.38293183 2014.91763309 2015.45233434 2015.9870356 2016.52173685 2017.05643811 2017.59113936 2018.12584062 2018.66054188 2013.31352932 2013.84823058 2014.38293183 2014.91763309 2015.45233434 2015.9870356 2016.52173685 2017.05643811 2017.59113936 2018.12584062 2018.66054188 2013.31352932 2013.84823058 2014.38293183 2014.91763309 2015.45233434 2015.9870356 2016.52173685 2017.05643811 2017.59113936 2018.12584062 2018.66054188 2013.31352932 2013.84823058 2014.38293183 2014.91763309 2015.45233434 2015.9870356 2016.52173685 2017.05643811 2017.59113936 2018.12584062 2018.66054188 2013.31352932 2013.84823058 2014.38293183 2014.91763309 2015.45233434 2015.9870356 2016.52173685 2017.05643811 2017.59113936 2018.12584062 2018.66054188 2013.31352932 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2018.08262228 2018.61963674 2019.1566512 2019.69366566 2014.32352108 2014.86053554 2015.39755 2015.93456445 2016.47157891 2017.00859337 2017.54560783 2018.08262228 2018.61963674 2019.1566512 2019.69366566 2014.32352108 2014.86053554 2015.39755 2015.93456445 2016.47157891 2017.00859337 2017.54560783 2018.08262228 2018.61963674 2019.1566512 2019.69366566 2014.32352108 2014.86053554 2015.39755 2015.93456445 2016.47157891 2017.00859337 2017.54560783 2018.08262228 2018.61963674 2019.1566512 2019.69366566 2014.32352108 2014.86053554 2015.39755 2015.93456445 2016.47157891 2017.00859337 2017.54560783 2018.08262228 2018.61963674 2019.1566512 2019.69366566 2014.32352108 2014.86053554 2015.39755 2015.93456445 2016.47157891 2017.00859337 2017.54560783 2018.08262228 2018.61963674 2019.1566512 2019.69366566 2014.32352108 2014.86053554 2015.39755 2015.93456445 2016.47157891 2017.00859337 2017.54560783 2018.08262228 2018.61963674 2019.1566512 2019.69366566 2014.32352108 2014.86053554 2015.39755 2015.93456445 2016.47157891 2017.00859337 2017.54560783 2018.08262228 2018.61963674 2019.1566512 2019.69366566 2014.32352108]
import matplotlib.pyplot as plt
import numpy as np
from sklearn.linear_model import LinearRegression
# Assuming boys_growth_state and girls_growth_state are your DataFrames
# Align indices of both DataFrames
boys_growth_state, girls_growth_state = boys_growth_state.align(girls_growth_state, join='inner')
# Plot growth for each age group
for age_group in boys_growth_state.columns:
# Exclude 'Season_Start', 'District', 'State' columns from comparison
if age_group in ('Season_Start', 'District', 'State'):
continue
# Extract x and y values for regression
x = boys_growth_state['Season_Start'].values.reshape(-1, 1)
y_boys = boys_growth_state[age_group].values.reshape(-1, 1)
y_girls = girls_growth_state[age_group].values.reshape(-1, 1)
# Remove NaN, infinity, and very large values
mask_boys = np.isfinite(y_boys) & (y_boys < 1e10)
mask_girls = np.isfinite(y_girls) & (y_girls < 1e10)
x = x[mask_boys & mask_girls].reshape(-1, 1)
y_boys = y_boys[mask_boys & mask_girls].reshape(-1, 1)
y_girls = y_girls[mask_boys & mask_girls].reshape(-1, 1)
# Fit linear regression models
model_boys = LinearRegression().fit(x, y_boys)
model_girls = LinearRegression().fit(x, y_girls)
# Predict y values using the models
y_pred_boys = model_boys.predict(x)
y_pred_girls = model_girls.predict(x)
# Plot boys' and girls' growth for the age group
plt.figure(figsize=(10, 6))
plt.scatter(boys_growth_state['Season_Start'], boys_growth_state[age_group], label='Boys')
plt.scatter(girls_growth_state['Season_Start'], girls_growth_state[age_group], label='Girls')
plt.plot(x, y_pred_boys, label='Boys Regression Line', color='blue')
plt.plot(x, y_pred_girls, label='Girls Regression Line', color='orange')
plt.title(f'Growth Comparison - {age_group} with Regression Line')
plt.xlabel('Season Start')
plt.ylabel('Growth')
plt.legend()
plt.show()
from sklearn.metrics import mean_squared_error
from sklearn.linear_model import LinearRegression
import numpy as np
# Assuming boys_growth_state and girls_growth_state are your DataFrames
# Align indices of both DataFrames
boys_growth_state, girls_growth_state = boys_growth_state.align(girls_growth_state, join='inner')
# Iterate over each age group column
for age_group in boys_growth_state.columns:
# Exclude 'Season_Start', 'District', 'State' columns from comparison
if age_group in ('Season_Start', 'District', 'State'):
continue
# Extract x and y values for regression
x = boys_growth_state['Season_Start'].values.reshape(-1, 1)
y_boys = boys_growth_state[age_group].values.reshape(-1, 1)
y_girls = girls_growth_state[age_group].values.reshape(-1, 1)
# Remove NaN, infinity, and very large values
mask_boys = np.isfinite(y_boys) & (y_boys < 1e10)
mask_girls = np.isfinite(y_girls) & (y_girls < 1e10)
x = x[mask_boys & mask_girls].reshape(-1, 1)
y_boys = y_boys[mask_boys & mask_girls].reshape(-1, 1)
y_girls = y_girls[mask_boys & mask_girls].reshape(-1, 1)
# Check if there are any remaining infinite or very large values
if not np.all(np.isfinite(y_boys)) or not np.all(np.isfinite(y_girls)):
print(f"Skipping age group {age_group} due to remaining NaN, infinity, or very large values.")
continue
# Fit linear regression models
model_boys = LinearRegression()
model_boys.fit(x, y_boys)
model_girls = LinearRegression()
model_girls.fit(x, y_girls)
# Predict y values using the models
y_pred_boys = model_boys.predict(x)
y_pred_girls = model_girls.predict(x)
# Print boys' and girls' growth for the age group
print(f'Age Group: {age_group}')
print('Mean Squared Error (Boys):', mean_squared_error(y_boys, y_pred_boys))
print('Mean Squared Error (Girls):', mean_squared_error(y_girls, y_pred_girls))
print('\nBoys Regression Coefficients:', model_boys.coef_[0][0])
print('Boys Regression Intercept:', model_boys.intercept_[0])
print('\nGirls Regression Coefficients:', model_girls.coef_[0][0])
print('Girls Regression Intercept:', model_girls.intercept_[0])
print('\n')
Age Group: All_Ages Mean Squared Error (Boys): 1935.5108987079927 Mean Squared Error (Girls): 291.8921433309042 Boys Regression Coefficients: 0.962229417301423 Boys Regression Intercept: -1937.0644368746555 Girls Regression Coefficients: 0.5068497754396911 Girls Regression Intercept: -1016.3802923862363 Age Group: 19&over Mean Squared Error (Boys): 3273.0441993196255 Mean Squared Error (Girls): 1590.7846068528154 Boys Regression Coefficients: 1.2089725330490013 Boys Regression Intercept: -2431.8977161472535 Girls Regression Coefficients: 1.085173549505563 Girls Regression Intercept: -2180.183727496564 Age Group: 17-18 Mean Squared Error (Boys): 1460.9358319288135 Mean Squared Error (Girls): 3150.8090194803067 Boys Regression Coefficients: 1.322573754157026 Boys Regression Intercept: -2664.889912528053 Girls Regression Coefficients: 1.1721827939438667 Girls Regression Intercept: -2353.637395794237 Age Group: 15-16 Mean Squared Error (Boys): 246.39277275146654 Mean Squared Error (Girls): 2146.8717557357604 Boys Regression Coefficients: 0.5896026757289666 Boys Regression Intercept: -1186.923910825153 Girls Regression Coefficients: -0.7775902367102362 Girls Regression Intercept: 1579.4866293665268 Age Group: 13-14 Mean Squared Error (Boys): 371.08035537756626 Mean Squared Error (Girls): 2327.075650527173 Boys Regression Coefficients: 0.2279776032345581 Boys Regression Intercept: -456.9170446844913 Girls Regression Coefficients: 0.38144076433481106 Girls Regression Intercept: -759.3894796737953 Age Group: 11-12 Mean Squared Error (Boys): 2626.2245212957546 Mean Squared Error (Girls): 873.0659704095862 Boys Regression Coefficients: 0.5557501191071347 Boys Regression Intercept: -1116.4565358215239 Girls Regression Coefficients: 0.6412688672452647 Girls Regression Intercept: -1284.0159996362538 Age Group: 9-10 Mean Squared Error (Boys): 6884.389888402031 Mean Squared Error (Girls): 1123.3456536802173 Boys Regression Coefficients: 1.7881431346874501 Boys Regression Intercept: -3600.468839825688 Girls Regression Coefficients: 0.3822313197579687 Girls Regression Intercept: -761.0016903591751 Age Group: 7-8 Mean Squared Error (Boys): 520.8155876299404 Mean Squared Error (Girls): 1974.4234503874304 Boys Regression Coefficients: 0.27755041357364224 Boys Regression Intercept: -556.2137216484246 Girls Regression Coefficients: 1.1090664012619196 Girls Regression Intercept: -2223.180163843313 Age Group: 6&U Mean Squared Error (Boys): 1033.1429128550478 Mean Squared Error (Girls): 9026.832813677196 Boys Regression Coefficients: 0.18984625058793886 Boys Regression Intercept: -377.975428712189 Girls Regression Coefficients: 1.420782564041547 Girls Regression Intercept: -2848.6917910137367 Age Group: Season_1_Compared Mean Squared Error (Boys): 0.0466382629382092 Mean Squared Error (Girls): 7.171244436773641 Boys Regression Coefficients: 0.9991129925897381 Boys Regression Intercept: 0.7783121516142728 Girls Regression Coefficients: 0.5347012556456204 Girls Regression Intercept: 937.4946029607443 Age Group: Season_2_Compared Mean Squared Error (Boys): 0.0 Mean Squared Error (Girls): 7.07793535067229 Boys Regression Coefficients: 0.9999999999999989 Boys Regression Intercept: 2.2737367544323206e-12 Girls Regression Coefficients: 0.5370144571863757 Girls Regression Intercept: 933.8504332242471
import pandas as pd
import matplotlib.pyplot as plt
from statsmodels.tsa.seasonal import seasonal_decompose
# Assuming boys_state and girls_state are your DataFrames
# Choose the appropriate frequency for the decomposition
# If 'Season_Start' contains only the year, freq should be 1
freq = 1
# Ensure the 'Season_Start' column is in datetime format for boys_state
boys_state['Season_Start'] = pd.to_datetime(boys_state['Season_Start'], format='%Y')
boys_state.set_index('Season_Start', inplace=True)
# Ensure the 'Season_Start' column is in datetime format for girls_state
girls_state['Season_Start'] = pd.to_datetime(girls_state['Season_Start'], format='%Y')
girls_state.set_index('Season_Start', inplace=True)
# Perform time series decomposition for boys_state
result_boys = seasonal_decompose(boys_state['All_Ages'], model='additive', period=freq)
# Plot the decomposition components for boys_state
plt.figure(figsize=(12, 8))
plt.subplot(4, 1, 1)
plt.plot(result_boys.observed, label='Observed (Boys)')
plt.legend(loc='upper left')
plt.subplot(4, 1, 2)
plt.plot(result_boys.trend, label='Trend (Boys)')
plt.legend(loc='upper left')
plt.subplot(4, 1, 3)
plt.plot(result_boys.seasonal, label='Seasonal (Boys)')
plt.legend(loc='upper left')
plt.subplot(4, 1, 4)
plt.plot(result_boys.resid, label='Residual (Boys)')
plt.legend(loc='upper left')
plt.tight_layout()
plt.show()
# Print the decomposition results for boys_state
print("Decomposition Results (Boys):")
print("Trend:")
print(result_boys.trend.dropna())
print("\nSeasonal:")
print(result_boys.seasonal.dropna())
print("\nResidual:")
print(result_boys.resid.dropna())
# Perform time series decomposition for girls_state
result_girls = seasonal_decompose(girls_state['All_Ages'], model='additive', period=freq)
# Plot the decomposition components for girls_state
plt.figure(figsize=(12, 8))
plt.subplot(4, 1, 1)
plt.plot(result_girls.observed, label='Observed (Girls)')
plt.legend(loc='upper left')
plt.subplot(4, 1, 2)
plt.plot(result_girls.trend, label='Trend (Girls)')
plt.legend(loc='upper left')
plt.subplot(4, 1, 3)
plt.plot(result_girls.seasonal, label='Seasonal (Girls)')
plt.legend(loc='upper left')
plt.subplot(4, 1, 4)
plt.plot(result_girls.resid, label='Residual (Girls)')
plt.legend(loc='upper left')
plt.tight_layout()
plt.show()
# Print the decomposition results for girls_state
print("\nDecomposition Results (Girls):")
print("Trend:")
print(result_girls.trend.dropna())
print("\nSeasonal:")
print(result_girls.seasonal.dropna())
print("\nResidual:")
print(result_girls.resid.dropna())
Decomposition Results (Boys):
Trend:
Season_Start
2011-01-01 987.0
2011-01-01 15781.0
2011-01-01 16625.0
2011-01-01 23927.0
2011-01-01 3208.0
...
2022-01-01 6802.0
2022-01-01 2800.0
2022-01-01 4403.0
2022-01-01 9348.0
2022-01-01 464932.0
Name: trend, Length: 636, dtype: float64
Seasonal:
Season_Start
2011-01-01 0.0
2011-01-01 0.0
2011-01-01 0.0
2011-01-01 0.0
2011-01-01 0.0
...
2022-01-01 0.0
2022-01-01 0.0
2022-01-01 0.0
2022-01-01 0.0
2022-01-01 0.0
Name: seasonal, Length: 636, dtype: float64
Residual:
Season_Start
2011-01-01 0.0
2011-01-01 0.0
2011-01-01 0.0
2011-01-01 0.0
2011-01-01 0.0
...
2022-01-01 0.0
2022-01-01 0.0
2022-01-01 0.0
2022-01-01 0.0
2022-01-01 0.0
Name: resid, Length: 636, dtype: float64
Decomposition Results (Girls):
Trend:
Season_Start
2011-01-01 87.0
2011-01-01 1504.0
2011-01-01 1194.0
2011-01-01 2543.0
2011-01-01 249.0
...
2022-01-01 852.0
2022-01-01 208.0
2022-01-01 430.0
2022-01-01 1200.0
2022-01-01 91254.0
Name: trend, Length: 636, dtype: float64
Seasonal:
Season_Start
2011-01-01 0.0
2011-01-01 0.0
2011-01-01 0.0
2011-01-01 0.0
2011-01-01 0.0
...
2022-01-01 0.0
2022-01-01 0.0
2022-01-01 0.0
2022-01-01 0.0
2022-01-01 0.0
Name: seasonal, Length: 636, dtype: float64
Residual:
Season_Start
2011-01-01 0.0
2011-01-01 0.0
2011-01-01 0.0
2011-01-01 0.0
2011-01-01 0.0
...
2022-01-01 0.0
2022-01-01 0.0
2022-01-01 0.0
2022-01-01 0.0
2022-01-01 0.0
Name: resid, Length: 636, dtype: float64